imaged. The most commonly used radiopharmaceutical in PET imaging is 18-F Flu- ... is simply a list indicating which detectors were involved in detecting each ...
MRI-BASED ATTENUATION CORRECTION FOR PET RECONSTRUCTION DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Jeffrey Steinberg, B.S. ***** The Ohio State University 2008
Dissertation Committee:
Approved by
Klaus Honscheid, Adviser Michael Knopp Richard Furnstahl Thomas Humanic
Adviser Department of Physics
c Copyright by
Jeffrey Steinberg 2008
ABSTRACT
In this study we proposed and developed a simple attenuation mapping approach based on magnetic resonance imaging (MRI) for the purpose of reconstructing positron emission tomography (PET) images in PET/MRI imaging devices. After experimental development, an in vivo calibration was performed by whole body scanning of five beagles on both a PET/CT and MRI. The attenuation was determined by using an automatic segmentation algorithm to segment regions of air, lung, soft tissue, and bone and assigning them values of 0.002 cm−1 , 0.030 cm−1 , 0.098 cm−1 , and 0.130 cm−1 respectively. The CT attenuated PET images and the MRI attenuated PET images were very similar, and average standardized uptake values (SUV) for most regions of interest differed by 1% - 6%. Also, mean relative differences (MRD) between the images were between 5% and 9% for most regions. The only exception is bone, where the three region MRI attenuated PET images had an SUV 9% less on average than the CT attenuation images, and the MRD averaged 14%. Also, additional segmentation of bone in the four region MRI attenuated PET images reduced the SUV difference to 3% and the MRD to 6%. However, the differences between the CT and 3 region attenuations were much smaller than the differences between CT attenuated and unattenuated PET images, which had average SUVs of 1-37% and MRDs of 10-45%. In particular the spine had an average SUV difference of 29% and MRD of 45%. Therefore, despite the improvements in the four region segmentation, ii
the three region segmentation without delineation of osseous tissues produces high quality images that are sufficient for most expected clinical purposes.
iii
VITA
1980 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Born - Cincinnati, Ohio 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.S. Physics, Case Western Reserve University, Cleveland, Ohio 2003-2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Graduate Teaching Associate, Physics Department, The Ohio State University, Columbus, Ohio 2005-Present . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Graduate Research Associate, Department of Radiology, Wright Center of Innovation in Biomedical Engineering, The Ohio State University, Columbus, Ohio
FIELDS OF STUDY Major Field: Physics Studies in: PET Reconstruction and Attenuation Correction
iv
Prof. Michael Knopp
TABLE OF CONTENTS
Page Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ii
Vita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
vii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
viii
Chapters: 1.
2.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1.1 1.2
Statement of Research . . . . . . . . . . . . . . . . . . . . . . . . . Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1 3
Background on Commonly Used Medical Imaging Systems . . . . . . . .
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2.1 2.2 2.3
2.4 2.5
Positron Emission Tomography . . . 2.1.1 PET Corrections . . . . . . . Computed Tomography . . . . . . . PET/CT Hybrid . . . . . . . . . . . 2.3.1 Design . . . . . . . . . . . . . 2.3.2 CT Attenuation Correction . 2.3.3 Clinical Example . . . . . . . Magnetic Resonance Imaging . . . . MRI/PET Hybrid . . . . . . . . . . 2.5.1 Development . . . . . . . . . 2.5.2 MRI Attenuation Correction 2.5.3 Clinical Example . . . . . . .
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5 10 16 18 19 19 23 25 30 31 33 36
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Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3.1 3.2 3.3
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37 38 39 39 43 44 44 45 47 47 52 54
Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4.1 4.2 4.3 4.4 4.5
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56 59 65 66 69
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5.1
Further Research . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3.4
3.5
3.6 4.
5.
PET/CT . . . . . . . MRI . . . . . . . . . . Registration . . . . . . 3.3.1 Background . . 3.3.2 Implementation Segmentation . . . . . 3.4.1 Background . . 3.4.2 Implementation PET Reconstruction . 3.5.1 Background . . 3.5.2 Implementation Analysis . . . . . . . .
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Beagle Images . . . . . . . Standardized Uptake Values Mean Relative Differences . Dynamic Scan . . . . . . . Discussion . . . . . . . . . .
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LIST OF TABLES
Table
Page
4.1
The weight and dose for each beagle
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56
4.2
Standardized Uptake Values (SUV) for CT attenuated PET (CT), MRI 3 region attenuated PET (MR1), MRI 4 region attenuated PET (MR2), and unattenuated PET (None) . . . . . . . . . . . . . . . . .
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Percent difference between CT attenuated PET SUVs and MRI attenuated PET SUVs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Mean Relative Differences . . . . . . . . . . . . . . . . . . . . . . . .
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4.3
4.4
vii
LIST OF FIGURES
Figure
Page
2.1
Scintillation inside detector crystal[13] . . . . . . . . . . . . . . . . .
7
2.2
Diagram of a Photomultiplier Tube[14] . . . . . . . . . . . . . . . . .
8
2.3
Images of the chest using, from left to right, MRI, CT, and PET . . .
9
2.4
Diagram of a PET system[18] . . . . . . . . . . . . . . . . . . . . . .
10
2.5
Three types of coincidences in a PET detector: True, Scatter, and Random . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12
Example of geometric sensitivities through positron annihilation events at 0 degrees and 45 degrees for annihilation events occurring in the middle and the edge of the detectors . . . . . . . . . . . . . . . . . .
13
Example of oblique angle of interaction which reduces the distance a photon can travel through the detector . . . . . . . . . . . . . . . . .
14
Above shows an axial slice of reconstructed PET images of a chest using (left) no attenuation and (right) CT attenuation. . . . . . . . .
15
Left - Germanium-68 Transmission Scan; Right - PET image using transmissions scan for attenuation correction[20] . . . . . . . . . . . .
16
2.10 Diagram of the CT[22] . . . . . . . . . . . . . . . . . . . . . . . . . .
18
2.11 Schematic of PET/CT scanner[23] . . . . . . . . . . . . . . . . . . . .
20
2.12 Mass attenuation coefficients as a function of photon energy[25] . . .
22
2.6
2.7
2.8
2.9
viii
2.13 Bilinear scaling factors to convert CT into attenuation map[26] . . . .
22
2.14 Left - CT; Right - Germanium-68 Transmission Scan[27] . . . . . . .
23
2.15 a. CT Scan; b. PET Scan; c. Fused PET/CT Image[28]
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2.16 Lymph node malignancy: a. CT Scan; b. PET Scan; c. Fused PET/CT Image[29] . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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2.17 Energy of two spin states in the hydrogen atom . . . . . . . . . . . .
26
2.18 Hydrogen precessing in a magnetic field . . . . . . . . . . . . . . . . .
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2.19 Diagram of MRI System[30] . . . . . . . . . . . . . . . . . . . . . . .
28
2.20 (Left) T1 weighted image; (Right) T2 weighted image . . . . . . . . .
29
2.21 Diagram of optical coupling[32] . . . . . . . . . . . . . . . . . . . . .
32
2.22 An example of an approximate attenuation map derived from an MRI image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
34
2.23 PET/MRI image of the brain[41] . . . . . . . . . . . . . . . . . . . .
36
3.1
3.2
3.3
3.4
PET and CT images in red and grayscale respectively for (left) images out of alignment and (right) aligned images after registration . . . . .
40
From left to right, drawn example images of an axial brain MRI for (1) the original MRI image, (2) the MRI image after linear registration to a target image, (3) the MRI image after nonlinear registration, and (4) the target MRI image of the brain . . . . . . . . . . . . . . . . . . . .
42
The figure shows an axial chest slice of the (a) Original MRI image; (b) CT image; (c) MRI image registered to CT; (d) Segmented and Registered MRI image . . . . . . . . . . . . . . . . . . . . . . . . . .
43
An axial chest slice for (a) the original MRI image; (b) image after threshold between high and low pixel values; (c) air and body segmented image after region cut of the lung, and (d) lung segmentation derived after subtraction of image b from image c . . . . . . . . . . .
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ix
3.5
Axial chest slices for (Left) original MRI image with ROIs drawn for air, lung, tissue, and spine; (Right) 3 region segmentation of air, lung, and tissue and 4 region segmentation of air, lung, tissue, and bone using the assigned values . . . . . . . . . . . . . . . . . . . . . . . . .
47
3.6
One dimensional projection of 2D slice of an object[55] . . . . . . . .
48
3.7
Commonly applied filters to Fourier transformed images used for filtered backprojection for PET reconstruction[56] . . . . . . . . . . . .
49
3.8
Comparison of reconstruction using (top) FBP and (bottom) OSEM[57] 50
3.9
(A) A coincident photon pair at an oblique angle; (B) Backprojection approximated as a series of parallel two dimensional coincidence events[60] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
53
3.10 ROI of the lung, heart, and spine for a CT attenuated PET slice of the chest of a beagle . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
55
4.1
4.2
4.3
4.4
CT topogram indicating the head, neck, chest, and hind slices used in figure 4.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
57
From left to right, PET slices of the head, neck, chest, and hind are shown (see figure 4.1). From top to bottom, the PET was reconstructed using no attenuation, MRI 3 region attenuation, MRI 4 region attenuation, and CT attenuation. The axial slices refer to the red lines drawn in figure 4.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
58
Above shows a chest slice of a (a) CT attenuation map, (b) MRI 3 region attenuation map, and (c) MRI 4 region attenuation map. These attenuation maps were used to reconstruct the (d) CT attenuated PET images, (e) MRI 3 region attenuated PET images, and (f) MRI 4 region attenuated PET images. The red arrows indicate a narrowing in the separation of the lungs due to misregistration, and the blue arrows indicate small changes in pixel intensity values in the spine due to the differing segmentations. . . . . . . . . . . . . . . . . . . . . . . . . . .
60
Average SUV of the heart for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None) . . . . . . . . . .
61
x
4.5
Average SUV of the liver for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None) . . . . . . . . . .
62
Average SUV of the lungs for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None) . . . . . . . . . .
63
Average SUV of the spine for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None) . . . . . . . . . .
64
Average SUV of the brain for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None) . . . . . . . . . .
65
Average SUV of the kidney for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None) . . . . . . . . . .
66
4.10 Average SUV of the bladder for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None) . . . . . . . . . .
67
4.11 The MRD of the heart between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none . . . . . . . . . . . . . . . . . . . .
68
4.12 The MRD of the liver between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none . . . . . . . . . . . . . . . . . . . .
69
4.13 The MRD of the lung between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none . . . . . . . . . . . . . . . . . . . .
70
4.6
4.7
4.8
4.9
xi
4.14 The MRD of the spine between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none . . . . . . . . . . . . . . . . . . . .
71
4.15 The MRD of the brain between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none . . . . . . . . . . . . . . . . . . . .
72
4.16 The MRD of the kidney between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none . . . . . . . . . . . . . . . . . . . .
73
4.17 The MRD of the bladder between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none . . . . . . . . . . . . . . . . . . . .
74
4.18 CT attenuated (red) and MRI 3 region attenuated (blue) dynamic PET scans for the heart, lung, liver, and spine. Each graph shows the SUV for each region versus the time in minutes from the beginning of the scan from 0 to 60 minutes. . . . . . . . . . . . . . . . . . . . . . . . .
75
xii
CHAPTER 1
INTRODUCTION
1.1
Statement of Research
In medicine today clinicians and researchers together have been seeking less and less invasive techniques to diagnose patients. The most common way to do this is through medical imaging, which uses an external field to image anatomical structure and function. There are a variety of different medical imaging techniques which view different characteristics of the body. Much work has been done to improve the quality of individual medical imaging systems, but there has also been much work done in the area of hybrid imaging. Hybrid imaging, the combination of two distinct imaging systems, is a highly useful diagnostic tool([1],[2],[3]). Each imaging system has its own advantages and disadvantages, and by combining two systems, one system can overcome the disadvantages of the other system[4]. A great example of this is the combination of Positron Emission Tomography(PET) and Computed Tomography(CT). PET gives functional data concerning metabolic processes and CT has good anatomical detail. PET’s weakness is its poor resolution, which by itself makes it very difficult to localize an area of interest. CT’s weakness is its lack of functional detail, so that problems such as lesions can be difficult or impossible to locate. By combining the two scanners, a radiologist can identify an anomaly and 1
localize it. Neither imaging modality alone could diagnose the problem accurately, so the information from both scanners is invaluable. PET/CT imaging systems are now a standard, and many hospitals do not even have a standalone PET scanner anymore. One of the most highly anticipated hybrid imaging systems is the combined Magnetic Resonance Imaging(MRI) and PET scanner([5],[6],[7]). MRI, like CT, provides good anatomical data, but it has many advantages over CT. MRI has high soft tissue contrast, can scan in all three dimensions, has high resolution, and can be used with a wide variety of contrast materials. An MRI/PET scanner could greatly increase the amount of information obtained from an imaging system. While an MRI/PET scanner could be potentially useful, a clinical scanner has not yet been developed. However, several prototypes have been developed in research groups around the world including a 7T MRI combined with an animal PET scanner that allows for simultaneous acquisition[8]. There are several problems associated with the development of MRI/PET, but attenuation correction is one problem in particular that has no clear solution yet. Attenuation correction is necessary in PET to obtain adequate images[9]. In PET imaging a radioactive tracer is injected into the patient which behaves like a biologically active molecule which is transported and used in certain regions of the body. The attenuation effect is a process in which photons passing through the body are absorbed or scattered before reaching the photon detectors causing the calculated position of the photons to be incorrect[10]. In the case of a scatter event, the scattered photon could reach a detector resulting in an incorrect reconstruction of the actual event. Since many of the photons are absorbed within the body, fewer photons from within the body reach the detectors resulting in higher signal intensity at the surface. 2
Also, low attenuation materials, such as air inside and outside of the body, do not contain the radioactive tracer, so attenuation correction is needed to calculate low signal intensity in low attenuation materials. Images without attenuation correction have poor contrast, poor resolution, and artifacts, such as high signal in the lungs and on the surface of the body. The attenuation can be determined using a transmission scan, which uses a rotating radioactive source to measure attenuation, or a CT image in the case of PET/CT hybrids. For MRI/PET systems, the attenuation must be derived using MRI. CT and transmission scans integrated into an MRI/PET pose new technological problems due to the sensitivity of a CT scanner in a high magnetic field. CT is sensitive to high magnetic fields due to the cathode tubes, which produce x-rays by accelerating charged particles. Also, many MRI/PET hybrid systems are being developed for simultaneous acquisition, which requires attenuation correction during PET acquisition. It is very important to use an MRI based attenuation correction, but unlike CT and transmission scans, MRI does not directly measure the attenuation. The research discussed in this paper concerns finding an effective way to measure attenuation from MRI. It involves approximating the attenuation by segmenting and categorizing tissue groups based on the pixel intensities of certain regions. The paper also discusses the effectiveness of these approximations and helps narrow down goals for more effective approximations.
1.2
Organization
Chapter 2 gives an overview of three commonly used medical imaging systems and the advantages and disadvantages of each. Chapter 3 gives an overview of multimodal
3
systems, including development, design, and function. Chapter 4 explains attenuation, why it needs to be corrected, and methods for obtaining corrections from both MRI and CT. Chapter 5 describes the experimental process of scanning the beagles, the process of registering, segmenting, and reconstructing the images, and the methods for analyzing the results. Chapter 6 states the results and discusses the meaning of the results. Chapter 7 restates the results and mentions possibilities for further research.
4
CHAPTER 2
BACKGROUND ON COMMONLY USED MEDICAL IMAGING SYSTEMS
2.1
Positron Emission Tomography
Positron Emission Tomography images the location of positron emitting molecules to create a three dimensional image of a patient. A positron is an antielectron, which is a positively charged electron. The existence of positrons was postulated by Paul Dirac in 1928 and later discovered experimentally in 1932 by Carl Anderson. Positrons are produced naturally in the upper atmosphere via cosmic ray interactions. Also, positrons are produced in positron-emitting radionuclides, which can occur naturally or be produced artificially using a cyclotron or generator[11]. Positron emission occurs when a proton is converted into a neutron as described by the process 1 1p
→10 n +01 e+ + ν
(2.1)
where p is the proton, n is the neutron, e+ is the positron, and ν is the neutrino. The production of a positron conserves charge, and a neutrino is produced as a result of the weak interactions of the decay. When a positron is emitted from the radionuclide, it will slow down due to scattering interactions with atoms within the body. Thus, the positron will travel a short distance before a positron-electron annihilation 5
occurs[12]. The annihilation of the positron and electron results in two photons. Due to conservation of momentum and energy, one photon cannot be produced. In the most common case two photons are emitted by e+ + e− = γ + γ
(2.2)
where the electrons are emitted in almost opposite directions. The photons are not emitted in exactly opposite directions due to a residual momentum of the positron and electron before annihilation. For a negligible residual momentum, the photons will be emitted in exactly opposite directions due to the conservation of momentum. The photons emitted in the annihilation interact with scintillators surrounding the patient. The interaction comes in the form of either the photoelectric effect or Compton scattering. Compton scattering is the scatter of a photon with an electron. This results in a lower energy photon and the emission of a high energy electron. In the photoelectric effect the photon is absorbed by the electron which causes the electron to be ejected from the atom. As the electron interacts with matter, it slows down and releases photons as shown in figure 2.1. The electron interacts with matter by depositing some of its kinetic energy by colliding and scattering off atoms. When the electron collides with another electron, it releases energy in the form of a photon as well as transferring some of the kinetic energy to the electron. Thus, multiple electron-electron collisions result in a cascade of electrons that emit a burst of photons within the material as they all deposit their energy within the material. When the excited electrons within the scintillator fall from the excited state to the ground state, they emit low energy photons in the range of visible light. This process is known as scintillation. Scintillators are used to facilitate the production of photons through a process known by fluorescence. Fluorescence is the process in which photons of a 6
Figure 2.1: Scintillation inside detector crystal[13]
certain energy absorbed within a material emit photons of lower energy. This process allows high energy photons such as gamma rays to be detected as low energy photons such as visible light. Scintillators are transparent materials that can stop a high energy photon and emit a burst of visible light through fluorescence. The visible light can be detected by photomultiplier tubes (PMT) (see figure 2.2), which amplify the signal through a series of electrodes, known as dynodes, at progressively higher voltages. When visible light hits the photoemissive cathode, it knocks electrons off and accelerates them to the next dynode of higher voltage. When these electrons hit the dynode, the extra kinetic energy gained in the acceleration knock even more electrons off the dynode. These then accelerate to the next dynode
7
Figure 2.2: Diagram of a Photomultiplier Tube[14]
to knock more electrons off and so on. This process of accelerating electrons through a series of voltage differences will produce a measurable current at the final dynode. The concept behind PET is to inject the patient with a positron-emitting radionuclide, or radiopharmaceutical. Radiopharmaceuticals act like other biologically active molecules that enter the body except that they emit radiation in the form of gamma rays, electrons, or positrons. These molecules are transported to certain areas of the body depending on the radiopharmaceutical used, and these areas of the body are imaged. The most commonly used radiopharmaceutical in PET imaging is 18-F Fluorodeoxyglucose (FDG)[15]. It is a glucose analog that is metabolized the same way in the body as simple sugars. Thus, FDG can highlight areas with high metabolic activity. For example, a malignant tumor is usually more active than surrounding tissue and will uptake more FDG. As a result the tumor will be much brighter in
8
Figure 2.3: Images of the chest using, from left to right, MRI, CT, and PET
the image than surrounding tissue making it easy to identify. An example of a PET image is shown in figure 2.3. A common PET system consists of a ring of scintillator blocks paired with photomultiplier tubes (PMT) that surround the patient(see figure 2.4). Some PET systems being developed use avalanche photodiodes (APD) instead of PMTs, which are used for high resolution, high sensitivity imaging as well as in MRI because they are not greatly affected by the high magnetic fields ([16],[17]). The photons produced from the annihilation are detected by the scintillator and PMT pair mentioned earlier. The two photons emitted in opposite directions can be detected together to form a coincidence. Then, one can estimate the position of the annihilation event by drawing a line between the two detectors of the coincidence. The coincidence is detected if each photon in the pair is detected within the coincidence timing window of the detector(usually a few nanoseconds). The information about the coincidences is processed electronically and is recorded in the form of a sinogram or listmode data.
9
Figure 2.4: Diagram of a PET system[18]
A sinogram gives the signal intensity for position versus angle, whereas listmode data is simply a list indicating which detectors were involved in detecting each coincidence. This data is then used to reconstruct the image. Assuming that two gamma rays are emitted in opposite directions, the interaction must lie on the line between the two detectors that detect the gamma rays. Reconstruction involves summing up all these lines of response (LOR) to produce the image.
2.1.1
PET Corrections
PET cannot directly measure concentrations of the injected radiopharmaceutical, but rather estimates the concentrations from the number of positron annihilations in the specific tissue where the radiopharmaceutical is. However, the likelihood that the positron annihilation will be detected as a coincidence depends on where the 10
annihilation event occurs. Without corrections the concentrations of the radiopharmaceuticals will be estimated incorrectly. Some common corrections include random and scatter, normalization, and attenuation correction. Not all coincidences detected in a PET detector are true coincidences as shown in figure 2.5. It is also possible that one or both photons in an annihilation are scattered off tissue within a patient. In this case the calculated position of the event would be along the LOR of the detected coincidence, which is not where the annihilation occurred. Some scatter events can be corrected for by measuring the energy of the detected photons. Since scattered photons have lower energy than the 511 keV photons emitted from the annihilation, one can correct for scatter by using an energy threshold high enough to filter out the scattered photons but allow the unscattered photons. A coincidence can also be detected from two separate annihilations that hit detectors within the coincidence timing window of the detector known as a random coincidence. Random coincidences are evenly distributed around the detector ring and add Gaussian noise to the detection. Thus, random coincidences can be corrected for by estimating the rate of random coincidences to calculate the noise in the detection, and then subtract the estimated noise from the result. Normalization is also used for corrections because photons from an annihilation are more likely to be detected in certain detector pairs more than others. Corrections need to be made for the geometry of the system since coincidences from positron annihilation photons from certain locations are more likely than other locations. For example, an event occurring in the outer ring of the detector will only be detected if the emitted photons travel in the same ring, or plane. An event occurring in the center ring of the detector is more likely to be detected because coincidences from the 11
Figure 2.5: Three types of coincidences in a PET detector: True, Scatter, and Random
annihilation photons can be detected in multiple rings. Figure 2.6 shows an example of photons emitted at 45 degrees and 90 degrees to the z axis both at the edge of the detectors and the center of the detectors. In the center coincidences can be detected at multiple angles relative to the z axis, whereas at the ends coincidences can only be detected at 90 degrees. Normalization also corrects for differences in detector response, such as crystal interferences, crystal efficiencies, and dead time factors[19]. Crystal interference factors describe the position of the scintillation crystals in the detector relative to the geometric factors of the PET system design. As shown in figure 2.7, an oblique angle of the LOR to the detectors are shown. At large oblique angles relative to the two detector pairs, the distance the photon can travel within the scintillator is different depending on the angle at which the photon hits the detector. Thus, some detector pairs have a higher probability of stopping a photon in the crystal because of the angle at which the photon hits the crystal. For example, a common PET detector has crystal dimension of 4.0 x 4.0 x 20 mm. If the photon does not hit the detector
12
Figure 2.6: Example of geometric sensitivities through positron annihilation events at 0 degrees and 45 degrees for annihilation events occurring in the middle and the edge of the detectors
at an angle, it can travel up to 20 mm before depositing its energy. However, at a 45 degree angle to the detector, it can only travel up to 5.6 mm before passing through an adjacent detector or escaping the detector ring. Since a scintillator is much less likely to stop the photon at a 45 degree angle, it has lower sensitivity and will detect fewer coincidences. So, this effect needs to be normalized. Crystal efficiencies refer to the intrinsic efficiencies of the two detectors in a coincidence event. The efficiency is affected by the applied voltage to the photomultiplier tubes and the properties of the scintillation crystal. Dead time refers to an interval of time when a block of detectors is unable to detect an event. This is because all detectors in a block are in the same coincidence circuit so that when an event is detected in one detector, no other detector in the block can detect an event for a short period of time. Dead time depends on the activity of the source, so the dead time effects will be small if the 13
Figure 2.7: Example of oblique angle of interaction which reduces the distance a photon can travel through the detector
activity is low. All of these factors are multiplied together to give a normalization sinogram, which is used to make corrections in the reconstruction. The most important correction for visually and quantitatively accurate PET images is attenuation correction. The attenuation effect is a process in which photons passing through the body are absorbed or scattered before reaching the photon detectors[10]. This results in an incorrect calculation of the distribution of radiation of the radiotracer within the body. It means that photons originating from the center of the body are less likely to reach the detector than those that come from the surface. The attenuation is described by I = I0 e−αx
14
(2.3)
Figure 2.8: Above shows an axial slice of reconstructed PET images of a chest using (left) no attenuation and (right) CT attenuation.
where I is the measured intensity, I0 is the incident intensity, x is the thickness of the material, and α is the attenuation coefficient. Attenuation correction is performed by generating a map of the attenuation coefficients in the body. Since CT measures attenuation directly, CT images are attenuation maps that can be used as correction factors in PET. Figure 2.8 shows both an unattenuated and CT attenuated image of the same slice of a patient. As can be seen in the figure, the unattenuated image is bright near the skin and in the lungs whereas the attenuated image is dark in those areas. Attenuation correction yields a much more accurate image and is absolutely essential for an acceptable image. Attenuation is generally measured by using radiation to measure the percentage of photons that penetrate a certain area of the body. This was typically done with a rotating germanium-68 source. Germanium-68 is a positron emitter, so it emits photons at the same energy as the PET tracer. By measuring the activity before and after the photons pass through the body, one can calculate how much the photons are attenuated. The source is rotated around the body to measure the attenuation
15
Figure 2.9: Left - Germanium-68 Transmission Scan; Right - PET image using transmissions scan for attenuation correction[20]
from multiple angles. By doing so a three dimensional image of the patient can be reconstructed. Figure 2.9 shows a transmission scan with the resulting PET image using the attenuation correction. Note that the only discernible structures in the transmission scan are air, lung, and other tissue, and this is sufficient to produce a visually high quality PET image.
2.2
Computed Tomography
Computed Tomography(CT) is a medical imaging method that generates a three dimensional image using x-rays. CT scans are also called computerized axial tomography(CAT) scans, but CT is more common nowadays. An example of a CT scan of the brain is shown in figure 2.3. The image of the brain is much clearer than the 16
PET scan and much more anatomical detail can be seen. Current CT systems can achieve a spatial resolution of 0.33 mm which is about 10 times better than most current PET systems[21]. CT reconstructs the object by measuring the attenuation characteristics of the tissues. For a material with a thickness x and linear absorption coefficient µ, the equation for the absorption is I = I0 e−µx
(2.4)
where I0 is the intensity of the beam. However, for a nonhomogeneous material, the equation for absorption is I = I0 e−
R
µ(x)dx
(2.5)
where the absorption coefficient is integrated over the thickness of the material. The fraction of the attenuated beam, I, is directly related to the thickness and composition of the material as well as the intensity of the x-ray beam. By taking scans at multiple angles, a two-dimensional slice of the object can be reconstructed. Then, the object can be scanned multiple times over the length of the object producing a three-dimensional image of the object. A typical CT setup is shown in figure 2.10. The patient is placed in the center while a collimator reduces the x-ray source to a thin fan beam. Detectors are placed opposite the source to measure the energy of the photons after passing through the body. The detectors consist of scintillators to produce light and photodiodes to detect the light as a current. From the current produced from the photodiodes, one can determine the energy of the incoming x-rays. The gantry, which consists of the x-ray source, collimator, and arc-shaped detector, rotates around the patient during data acquisition. The attenuation characteristics at each angle are measured and the 17
Figure 2.10: Diagram of the CT[22]
computer reconstructs the image. CT is useful for producing good quality images, but it is not very effective for functional imaging, the measurement of metabolic processes within the body.
2.3
PET/CT Hybrid
With the success of PET and CT imaging, researchers have sought out ways to improve these diagnostic tools. Historically, a PET study would be examined in conjunction with a CT scan to diagnose a patient. That is because PET and CT benefit the diagnosis in different ways. PET provides high sensitivity functional information of lesions while CT provides detailed information about the location, shape, and size of these lesions. CT has difficulty differentiating between lesions and normal tissue, so it is important to use PET in conjunction with a CT scan. However, 18
it can be difficult to know the exact location of the pathology just from looking at the PET and CT systems separately. For this reason, techniques to combine and overlap the images have been sought. Although it is possible to simply place one image on top of another, the resulting image may inaccurately identify the location of pathology. A combined PET/CT system that can image the patient with PET and CT almost simultaneously was developed to minimize these errors. The development and use of PET/CT systems are described in the next few sections.
2.3.1
Design
In the PET/CT scanner, the PET and CT components are mounted on the same aluminum support. The CT scanner is mounted on the front and the PET scanner is mounted on the back(see figure 2.11). Due to the short CT scan time, it is not necessary to have simultaneous data acquisition for PET and CT. A full body scan for PET takes about 45 minutes, whereas a full body CT scan takes only 5 minutes. The acquisition and reconstruction of data for PET and CT scanners are not done together, but are each done independently of each other. This simplifies the design since a new method of acquisition and reconstruction is not required to produce an image. The PET and CT scanners acquire and reconstruct the data separately, and then the two separate images are registered to produce the final image.
2.3.2
CT Attenuation Correction
Another important advantage of using a combined PET/CT scanner is the ability to use the CT image to perform attenuation corrections in the PET data. This cut down the time considerably since PET transmission scans can take nearly as long as a PET scan to perform[24]. It also cut down on cost by eliminating the 19
Figure 2.11: Schematic of PET/CT scanner[23]
need to incorporate a PET transmission scanner within a PET scanner. Due to the convenience of using a CT scan for attenuation correction along with the advantages of combining PET and CT, PET/CT hybrid scanners are quickly replacing the need for a standalone PET scanner. CT attenuation correction cannot be done directly with the original CT scan, so some minor modifications to the images need to be done. First, the CT scan is in Hounsfield units (HU), which is a scale developed by Godfrey Hounsfield for viewing CT images. It converts the original images containing the attenuation information for the object being scanned into a scaled image which optimizes visualization. For a material X with linear attenuation coefficient, µX , the corresponding HU value is HU =
µX − µH2 O ∗ 1000 µH2 O − µair
20
(2.6)
If we assume that the attenuation of air is negligible compared to the attenuation of water, then the equation above can be converted into µX = µH2 O ∗
(HU + 1000) 1000
(2.7)
where µX and µH2 O are the attenuation values of substance X and water at CT energies. Kinahan et. al.[25] demonstrated that the attenuation map for an effective CT photon energy of 70 keV can be converted to the PET energy of 511 keV to provide accurate values for the attenuation correction factors. The algorithm is effective since the ratio of the mass attenuation coefficient at 70 keV to that at 511 keV is the same for most tissues. Thus, the resulting attenuations from equation 2.7 only need to be multiplied by the difference in attenuation of water, µX (P ET ) = µX (CT ) ∗
µH2 O (P ET ) = 0.52 ∗ µX (CT ) µH2 O (CT )
(2.8)
The coefficient is higher for bone, so bone must be attenuated at a different factor than other tissues(see figure 2.12). In that case, the attenuation should be multiplied by 0.40 instead of 0.52. This is done by using a bilinear scaling factor as shown in figure 2.13. The two slopes in the image refer to the attenuation ratios of CT to PET. It is assumed that substances with high attenuation are likely to be bone and thus have a smaller slope. Sometimes iodine is used as a contrast agent in CT. Since iodine has a high attenuation, normal tissue that uptakes the iodine can have an HU as high or higher than bone. In this case, a different scale must be used as indicated in figure 2.13. Figure 2.14 shows an example of a CT and a transmission scan. Both images show the main structural regions of air, lung, and other tissue. 21
Figure 2.12: Mass attenuation coefficients as a function of photon energy[25]
Figure 2.13: Bilinear scaling factors to convert CT into attenuation map[26]
22
Figure 2.14: Left - CT; Right - Germanium-68 Transmission Scan[27]
2.3.3
Clinical Example
One example of the advantages of a PET/CT scanner is shown in figure 2.15. The figure shows a CT scan, PET scan, and the registered image of the two scans. The PET scan shows an unusual amount of FDG uptake in the left nymph node. The CT scan shows no abnormalities. With the PET scan alone, it would be difficult to accurately find the location of the malignancy. As can be seen in the figure, a fused PET/CT image can clearly identify the location of the tumor. This is just one example of the advantage of using a PET/CT scanner to improve the diagnoses of patients. Another example of the advantage of a PET/CT scanner is that of a patient with a history of ovarian cancer. Figure 2.16 shows transverse images of the pelvis with PET, CT, and combined PET/CT. The CT scan shows an enlarged lymph node, but it is difficult to determine the malignancy of the area. The FDG PET scan shows an abnormal uptake value in the region, but the precise location of the accumulation of FDG is uncertain. In the combined PET/CT image, it is shown that the area of accumulated FDG is the location of the enlarged lymph node. This indicates that the 23
Figure 2.15: a. CT Scan; b. PET Scan; c. Fused PET/CT Image[28]
Figure 2.16: Lymph node malignancy: a. CT Scan; b. PET Scan; c. Fused PET/CT Image[29]
tissue is a tumor. Also, with the combined PET/CT image, a small lymph node with increased FDG uptake is shown on the other side. No abnormal activity was shown in either the PET or CT image, but the combined image showed suspicious activity. After surgical inspection, the presence of cancerous cells in both lymph nodes was confirmed.
24
2.4
Magnetic Resonance Imaging
Magnetic Resonance Imaging(MRI) is a system that creates high quality images of the inside of the human body. The resolution of current MRI systems is 0.1 mm, which is five times better than the resolution of most CT scanners. An example of an MRI scan of the brain is shown in figure 2.3. As one can see, MRI gives an image with much higher detail than both CT and PET. MRI creates images by measuring the magnetic properties of atoms inside the body. In the absence of a magnetic field, the nuclei of the atoms will spin and precess in random directions. In a magnetic field the nuclei of the atoms will tend to line up with the magnetic field. This is due to spin, an intrinsic property of quantum mechanics. The overall spin of the nucleus is determined by the number of protons and neutrons in a given atom. Protons and neutrons pair with each other to give zero overall spin. In the case of an odd number of protons or neutrons, the unpaired proton or neutron will contribute an overall spin to the atom. A non-zero spin will cause the atom to have a magnetic moment, µ, by µ = γI
(2.9)
where γ is the gyromagnetic ratio and I is the spin. The gyromagnetic ratio is the ratio of the magnetic dipole of an atom to its angular momentum. For hydrogen-1, the most commonly imaged atom in MRI, the spin is 1/2. The angular momentum associated with the spin has two possible states, + and -. The z component of the angular momentum, Iz , is Iz = m~ 25
(2.10)
Figure 2.17: Energy of two spin states in the hydrogen atom
where m is the magnetic quantum number and ~ is the reduced Planck constant. The magnetic quantum number is ± the spin, so for hydrogen, it’s ±1/2. So, for an applied magnetic field oriented along the z axis, the energy is E = −B · µ = −µz B
(2.11)
E = −m~γB
(2.12)
and substituting µz = γIz ,
The nucleus of the hydrogen atom has two energy states as shown in figure 2.17. Due to the difference in energy states, hydrogen has a higher energy when aligned against the applied magnetic field than with the field. Therefore, since the lowest energy state is the most probable, more hydrogen atoms will align with the field than against the field. This means that for a tissue with a large number of hydrogen atoms, the overall 26
Figure 2.18: Hydrogen precessing in a magnetic field
magnetization will align with the field. This is what is measured with MRI. Note that in figure 2.17, the difference in energy states becomes larger in larger magnetic fields. The result is a higher magnetization and thus a higher signal to noise ratio. A diagram of the MRI system is shown in figure 2.19. The patient is placed within the bore of the magnet which produces a strong, constant magnetic field that runs straight down the center of the tube. Magnetic fields for clinical use are usually between 0.5T and 2T, but a 7T MRI machine is being used for research purposes at the Wright Center of Innovation at The Ohio State University. The radio frequency(RF) coil produces pulses perpendicular to the main magnetic field. The gradient coil produces a relatively small magnetic field in three directions that can
27
Figure 2.19: Diagram of MRI System[30]
alter the magnetic field at a specific location. The MRI system also consists of pickup coils that measure the tissue’s response to the applied magnetic field. The RF pulse causes the hydrogen atoms to precess at a specific frequency of resonance called the Larmor frequency. The Larmor frequency is defined as ω = γB
(2.13)
where B is the magnitude of the magnetic field and γ is the gyromagnetic ratio. It depends on the specific atom being imaged and the strength of the magnetic field. The gradient coils then create a change in the magnetic field within the bore of the magnet thus changing the Larmor frequency of the hydrogen atoms slightly. The RF pulse is emitted at the Larmor frequency to maximize the transverse component of the magnetization, so by tuning in the frequency, one can measure responses from one slice at a time. This is because an RF pulse that is not at the resonant, or 28
Figure 2.20: (Left) T1 weighted image; (Right) T2 weighted image
Larmor, frequency of the hydrogen atom will not noticeably precess. Hydrogen is often used in MRI since it is the most abundant atom with spin in the human body. Other commonly imaged atoms are phosphorus and sodium, which have different gyromagnetic ratios than hydrogen. As a result, the RF pulse must be tuned into the Larmor frequency of these atoms in order to image them. MRI creates images of the patients by measuring the T1 and T2 times of hydrogren atoms within certain tissues. The T1 time is known as the spin lattice relaxation time, and it determines the rate at which the spinning protons realign with the external magnetic field. The T2 time is known as the spin spin relaxation time, and it determines the rate at which the excited protons go out of phase with each other. Both times can be used for imaging the patient. T1 weighted images produce high signals in fat, bones, and hemorrhages. T2 weighted images produce high signals in cysts, tumors, and cranial fluid. Depending on the area being imaged, either T1 29
or T2 weighted images may be used. Figure 2.20 shows an example of T1 and T2 weighted images of the brain. The T1 and T2 images have some common features such as a low pixel intensity in the skull and a high pixel intensity in the skull. However, they differ in that the T2 weighted image has bright signals in the tumor and intercranial fluids and the T1 weighted image is dark in those areas. Therefore, MRI is capable of extracting different kinds of information from a single patient using different sequences. This gives MRI a large degree of versatility in its use.
2.5
MRI/PET Hybrid
With the success of PET/CT imaging, researchers have seen the advantage of using multi-modal imaging over using a single imaging system. As a result the idea arose of expanding hybrid imaging to combining other systems, such as MRI and PET. With the high spatial resolution of MRI and the high functional information acquired from PET, MRI/PET has high clinical implications. MRI has an advantage over CT since it is able to distinguish between the concentrations of some important molecules. This gives more information than simply the tissue attenuation characteristics of CT. Also, MRI can identify anatomical anomalies and locate them precisely. PET is useful for showing the functional changes in the anomaly. For example, an MRI can identify the location and size of a tumor with great precision, and PET can identify the metabolic activity of the tumor. So, if the size of a tumor remains the same size after therapy, an MRI alone would identify no change. However, a PET image may show a significant reduction in metabolic activity in the location of the tumor, which shows a positive response to therapy. An MRI alone would show the therapy to be ineffective without the use of PET. PET may show that the activity has reduced, but
30
it cannot accurately identify the location and size of the tumor without MRI. That is why it is important to combine the two techniques to effectively diagnose the patient.
2.5.1
Development
The development of a PET/CT hybrid was successful since the PET and CT systems did not interfere significantly with each other. Combining both modalities was feasible since the imaging radiation of each system has similar physical characteristics and energy. However, the combination of MRI with PET is very different in both energy and physical characteristics. PET involves the detection and localization of photons from a positron decay and annihilation, whereas MRI involves the detection of resonances in a strong magnetic field. Also, the high magnetic field of MRI poses difficulties for some of the instrumentation in PET. Current PET systems use scintillation detectors with PMT’s, but PMT’s are extremely sensitive to magnetic fields. Any wires or electronics within an MRI system will pick up electrical signals from the changing magnetic field. The PET system can also affect the MRI system negatively since the main magnetic field in MRI needs to have high homogeneity. A PET system inside the bore of the magnet can cause significant distortions of the geometry of the MRI images depending on the susceptibility, conductivity, and geometry of materials in the PET system. There have been two main approaches pursued in the development of MRI/PET systems. One approach is to develop a system that allows simultaneous acquisition of both PET and MRI data[31]. Simultaneous acquisition in PET/CT systems was less important since CT scans were very short. With the lengthy acquisition times of PET and MRI imaging, acquiring data at the same time helps in the study of dynamic
31
Figure 2.21: Diagram of optical coupling[32]
processes and the correlation of these processes. This approach consists of coupling the light from a scintillator inside the MRI magnet to the electronics and PMT’s placed outside the bore of the magnet. This is achieved by placing the scintillators inside the magnet and using optical fibers to couple the scintillators to the PMT’s outside the MRI system(see figure 2.21). A second approach to this hybrid system is to have the data acquisition between PET and MRI to alternate in quick succession within the same frame of reference. With this approach it is possible to place the PET detectors inside the bore of the magnet. When the MRI system is taking data, the PET system will remain shut off, and when the PET system is taking data, the MRI system will be shut off. The only requirement is to have PET detectors that can withstand the high, constant magnetic field within the MRI system. Conventional PMT’s cannot withstand such a high magnetic field, so a semiconductor detector such as an avalanche photodiode(APD) is required[33]. An APD is a semiconductor that shows an internal gain effect of about 100 due to the avalanche effect. The avalanche effect is a form of current multiplication that 32
occurs within insulating or semiconducting solids, liquids, or gases when an electric field is applied that is high enough to accelerate electrons to a velocity capable of knocking electrons free when they strike an atom. The result is a cascade effect similar to that found in PMT’s. The APD’s have much lower sensitivity to magnetic fields than PMT’s and can potentially be placed within the bore of the magnet[34]. Some APD’s have been shown to be insensitive to magnetic fields as high as 4.8 tesla[17]. Also, APD’s are smaller than PMT’s, so it is possible to increase the number of detectors in the ring. One advantage of having a PET detector inside the magnet is that the magnetic field under certain orientations would shorten the range of positrons in tissue. That is, the positrons would travel a shorter distance before annihilation allowing for better localization of the positron decay. However, high field strengths are necessary for any significant improvement in resolution[35].
2.5.2
MRI Attenuation Correction
The need for MRI based attenuation correction is similar to that of CT attenuation correction in PET/CT hybrids. Attenuation correction using MRI eliminates the need for a separate transmission scan as well as the inclusion of a transmission scanner. Also, similar to CT attenuation correction, MRI does not measure PET attenuation directly and needs to be converted. However, unlike CT attenuation correction, MRI does not measure attenuation at all, which makes it particularly difficult to use for attenuation correction. The two alternatives to using an MRI based attenuation correction are the incorporation of either a transmission scan or a CT scan. As mentioned earlier, transmission scans take a long time to perform and would
33
Figure 2.22: An example of an approximate attenuation map derived from an MRI image
require additional hardware to the MRI/PET system. The addition of a CT poses new technological problems due to the sensitivity of the x-ray cathode tubes to high magnetic fields[36]. The most practical solution is to use information from an MRI to provide attenuation corrections for PET imaging. Although MRI does not measure the attenuation, it does provide detailed anatomical structures that can be used to estimate the attenuation. As seen in figure 2.22, there are three clearly defined regions: air, lung, and tissue. Since the pixel intensities in each region vary greatly, it is simple to segment the three regions. Then, values can be assigned to each region based on the expected average attenuation of each region. If necessary, more tissues can be segmented and assigned their corresponding attenuation value. One region of particular interest is the bone. Muscles, fat, and organs such as the heart, brain, and liver have very similar attenuation values, so assigning a single value for all regions
34
is acceptable. However, bone has a significantly higher attenuation than the other tissues, so it may be important to segment that as well. Bone segmentation in MRI is a strongly researched area, especially in the area of MRI based segmentation. Zaidi et al[37] discussed a few segmentation algorithms of the brain in his paper, which includes segmentation of the skull. The segmentation algorithm consisted of first using a fit ellipse based method, which approximates the head as an ellipse and uses that approximation of an initial reconstruction. The brain tissue is segmented from the resulting PET image by removing the skull and extracranial tissue from the image due to their low signal intensity relative to the brain. Then, the skull is approximated by using an automated contour detection method, which takes into account higher attenuation material. The method estimates the outline of the skull from the emission sinogram and assumes a uniform skull thickness within the estimated shape. Once the brain and skull are segmented, the remaining tissue of the head is assumed to be extracranial tissue. Finally, the sinuses are segmented easily due to their low signal intensity in MRI. Each region is then assigned an attenuation value based on CT averages. The skull was able to be segmented due to its relatively consistent shape and thickness. However, bones in other parts of the body, such as the spine, ribs, and hip, are much more difficult. There has been significant research in this area ([38], [39], and [40]), but these methods tend to focus on narrow target areas such as the knee and hips. No one has yet generated an automated method for full body bone segmentation. However, in the application of MRI based attenuation corrections, additional segmentation of bone may be unnecessary.
35
Figure 2.23: PET/MRI image of the brain[41]
2.5.3
Clinical Example
A clinical example of combining PET and MRI is shown in figure 2.23. In the PET image high contrast is shown near the center of the brain, but the structure that the arrow is pointing to cannot easily be identified. In the MRI scan it is difficult to identify any specific structure near the center of the brain due to the low contrast. However, with the combined image, the structure can be localized with the MRI scan and be identified as the pituitary gland. Neither PET nor MRI alone would be able to identify the structure, but together, it is clear.
36
CHAPTER 3
MATERIALS AND METHODS
The study was designed by an interdisciplinary team and approved by IACUC, an animal experimentation committee. Five beagles with no known preexisting conditions were each scanned on both a PET/CT and MRI system. Each scan was performed only one time, and the beagles were not euthanized after scanning.
3.1
PET/CT
The PET examinations were performed on a Siemens Biograph 16 HI-REZ PET/CT system with the beagles imaged in the prone position. The beagles were anesthetized for the purpose of keeping the beagles immobile during scanning. Anesthesia was induced with 1 mg/kg diazepam and 1 mg/kg xylazine followed by maintenance of isoflurane inhalation (1-3%). Each beagle was placed in the prone position on the table and kept immobile using sheets for support on the sides and a strap over the back. After the beagle was secure, a routine survey scan was performed initially followed by a full body CT scan (matrix size 512 x 512; 507 slices; 3 mm slice thickness). A dynamic scan of one bed encompassing the lungs and upper abdomen was obtained for 1 hour. The dynamic scan was a PET scan taken every minute for a total of 60 time points. After 1 minute of acquisition, approximately 3 mCi of 18 37
FDG (Fluoro-2-Deoxyglucose) was injected. After the dynamic scan, a full body CT and PET scan were taken. The PET scan used a total of 6-7 beds. A bed is a 3D acquisition of the object being scanned at one location. Each bed is 162 mm long, 81 slices, with a 40 mm overlap and 2 mm slice thickness. Since it is only 162 mm long, multiple beds must be taken to scan the entire beagle. The overlapped slices taper off in signal intensity at the ends so that adjacent beds can be summed at the 40 mm overlap range. Two beagles were scanned with 6 beds, 371 slices, and three beagles were scanned with 7 beds, 429 slices due to the differing beagle sizes. The scan was a whole body scan covering the entire length of the beagle except for the tail and hind legs. The acquisition times for the CT and PET scans were approximately 20 seconds and 20 minutes respectively.
3.2
MRI
The MRI examinations were performed on a Philips Achieva 3 Tesla MRI system using the standard spine coil with beagles imaged in the prone position. The beagles were anesthetized using 1 mg/kg diazepam and 1 mg/kg xylazine followed by maintenance of isoflurane inhalation (1-3%). After routine localization images, a T1 -Weighted Fast Field Echo Thrive sequence (TR/TE = 2.7 ms/1.22 ms; field of view = 300 x 300 mm2 ; matrix 256 x 256 with in plane resolution 1.17 x 1.17 mm2 ; 140 slices; 2 mm slice thickness, contiguous slices) was acquired. The fast field echo sequence uses a series of 90 degree RF pulses taken with a short TR/TE to measure the T1 relaxation times. The Thrive scan was followed by a T2 -weighted Turbo Spin Echo sequence (TR/TE = 16964 ms/80 ms; field of view = 300 x 300 mm2 ; matrix 256 x 256 with in plane resolution 1.17 x 1.17 mm2 ; 35 slices; 3 mm slice thickness, 5
38
mm gap). The turbo spin echo series uses a 90 degree pulse followed by several 180 degree rephasing pulses to measure the T2 relaxation times. The acquisition times for the Thrive and T2 -weighted sequences were 11 minutes 12 seconds and 3 minutes 6 seconds respectively. The sequences were repeated three times for three different anatomical areas. The first anatomical area was from the tip of the nose to the neck, the second was from the neck to the upper abdomen, and the third was from the upper abdomen to the rear. The tail and rear limbs were not included in the scan. Each scan has a 20-40 mm overlap to facilitate the process of combining the three sections into one whole body image. Also, the overlap helps eliminate artifacts that can appear near the ends of the scan due to lack of signal strength.
3.3 3.3.1
Registration Background
Registration is the process of overlapping one image on top of the other. In doing so, one must get the position in all dimensions and the rotation in all planes correct. Given a PET and CT scan, it is highly unlikely that the two scans will overlap completely due to changes of the patient’s position during each scan. Without registration, it would be difficult to identify a change in function without knowing precisely where it is localized and without knowing the underlying cause[42]. Figure 3.1 shows PET/CT images overlapped before and after registration. In the figure the body is positioned and rotated in order for the two images to overlap. The earliest and simplest form of registration is known as fiducial and landmark registration([43],[44]). It involves the matching of two images based on specific points
39
Figure 3.1: PET and CT images in red and grayscale respectively for (left) images out of alignment and (right) aligned images after registration
within the image. These points can be chosen by manually specifying key points within the body. The points can also be identified with a fiducial marker. A fiducial marker is an external object that is placed on or within a patient before scanning. Fiducial markers are designed to be easily visible and detectable within the imaging modality used. A common fiducial marker is a stereotactic frame, which is an object that is screwed into the patient’s skull. It has been used as the gold standard in accuracy in registration for neurosurgery. A less invasive fiducial marker is a skin marker, which can be placed and removed easily anywhere on the body. Other fiducial markers include foam molds, head frames, and dental adapters[45]. Registration can also be done by placing markers in easily identifiable anatomical parts, or landmarks. This is done without the inclusion of external objects and are performed after the scan has been completed. The markers can also be placed on geometrically significant
40
locations such as corners. Both the fiducial markers and landmarks serve the same purpose of identifying a common location in both images. Registration is done by simply matching the markers of one image to the markers of the other image. This is the simplest form of registration because no image information is necessary for registration. One drawback of this method is that it must be done manually, which is very time consuming. It also requires a fairly large number of markers in order to yield accurate registration. This is because only pixels near the location of the markers are registered accurately, and pixels far from the location of the markers are likely to be inaccurate. Despite these drawbacks landmark registration is still commonly used today for registration. Repositioning and rotations to match images is known as linear registration. It assumes that two images have the exact same shape and structure, but differ only by their position and orientation in a data space. However, in practice patients will not have the same shape and structure internally due to involuntary movement of internal organs. For example, a CT could be acquired while the patient is at full inhalation and a PET image could be obtained over a long period of time, which averages out to partial inhalation. The lungs will appear larger in the CT image due to the larger volume of air in the lungs. In order to correct for this mismatch, registration of the CT to PET should reduce the size of the lungs relative to the surrounding tissue to match the images appropriately[46]. This method of registration is commonly called nonlinear due to the deformations of the image required for registration[47](see figure 3.2). Nonlinear registration would register patient scans by first doing a rigid registration to approximately line up the two images. Then, deflation of the lungs with nonlinear registration would be used to match the chest of the two patients. 41
Figure 3.2: From left to right, drawn example images of an axial brain MRI for (1) the original MRI image, (2) the MRI image after linear registration to a target image, (3) the MRI image after nonlinear registration, and (4) the target MRI image of the brain
One common implementation of nonlinear registration is spline registration[48]. A spline is a series of curve segments that are joined to form a single continuous curve. Spline registration deforms the image using control points that are placed in specific locations. In the case of a thin plate spline(TPS) registration, the plane in which the image lies is modeled to be like a thin sheet of metal. The TPS warps the plane by bending the sheet. The x and y components of the control points are considered separately, so an x-directional model and y-directional model are needed to warp the 2D image. A smoothly varying deformation between the control points is derived by minimizing the bending energy of the analogous physical model of a thin sheet of metal bending. The result is that the spline deforms nearby points greater than far away points allowing for localized registrations.
42
Figure 3.3: The figure shows an axial chest slice of the (a) Original MRI image; (b) CT image; (c) MRI image registered to CT; (d) Segmented and Registered MRI image
3.3.2
Implementation
In this study registration is performed by a combination of landmark registration and TPS nonlinear registration. Registration of the PET and MRI images was done using the Medical Image Processing, Analysis & Visualization(MIPAV)[49] software package. Due to the difficulty of registering MRI to PET, the MRI was registered to the CT, and the CT was already registered to PET by the PET/CT imaging system. After the MRI and CT images were loaded in the software, a landmark based registration using a thin plate spline was used for nonlinear registration of the two modalities. In order to achieve adequate registration between the modalities, 30-50 points were manually chosen in each image for the landmark registration. One axial slice of the registered MRI image is shown in figure 3.3.
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3.4 3.4.1
Segmentation Background
Image segmentation is an important processing step in many applications, such as for the identification of anatomical regions of interest for diagnosis, treatment, or surgery planning, and for a preprocessing step for multimodality image registration.[50] Segmentation is also useful for measurements of tumor volume and its response to therapy, but differentiation between tissues such as edema, necrotic, and scar tissue can be difficult since they have similar MR characteristics. However, combining structural imaging such as MRI with functional imaging such as PET can differentiate between these important regions. There are several commonly used segmentation techniques[51] each with its own advantages and disadvantages. The quickest and most efficient way to segment an image is through thresholding methods. The simplest histogram method would be a simple threshold, t, for an image, I. Given a pixel, p, the image can be segmented into two regions, I(p) < t and I(p) ≥ t. One example of this is the segmentation of bone from CT images for the generation of attenuation maps.[25] In this case, the threshold is set to 300 Hounsfield units so that the region of I(p) ≥ t is the bone. The bone and other tissues, I(p) < t, are then scaled with different factors to convert the 70 keV CT to a 511 keV attenuation map. Thresholding with multiple values is also possible, but it is difficult to find significant peaks and valleys in the image. Although thresholding is fast, it is no good at segmenting regions of similar attenuation. One important application of segmentation is in the generation of attenuation maps in MRI. This is done after the MRI images are registered to the PET and CT images. MRI does not measure attenuation, so an approximation of the attenuation 44
needs to be made from the anatomical structure found in the MRI. The most common way of extracting attenuation information from the MRI is to group tissues of similar attenuation together. The four groups of tissues segmented were air, lung, bone, and soft tissue. The soft tissue includes skin, muscle, fat, heart, liver, brain, and other tissues. The bone segmented includes both compact and cancellous bone. Compact bone is the dense portion on the surface of the bone that supports the weight of the body. Cancellous bone is the spongy, low density bone that fills the bulk of the internal bone structure, and includes the bone marrow. Although these two types of bone have different attenuation characteristics, they will be combined in the segmentation. The resulting segmentation of bone will be assigned an attenuation coefficient value equal to the average attenuation of the two types of bone.
3.4.2
Implementation
An algorithm for segmentation of the MRI images was developed in Interactive Data Language (IDL)[52], a commonly used programming language in medical imaging due to its ability to process large amounts of data, such as image processing. The first step in the attenuation is to segment the air in MRI from body tissues and cavities. Since air has a very low pixel value in all modalities, it can be easily segmented using a threshold with a maximum allowed value. However, lungs have a low pixel value as well, which can range from as low as air to as high as soft tissue. Figure 3.4 shows the various steps of the segmentation process. Figure 3.4a shows the original MRI of an axial slice of the beagle’s chest. A simple threshold of the original MRI is used to separate tissues from air. As seen in figure 3.4b, there are regions within the body labeled as air and outside the body not labeled as air. Most
45
Figure 3.4: An axial chest slice for (a) the original MRI image; (b) image after threshold between high and low pixel values; (c) air and body segmented image after region cut of the lung, and (d) lung segmentation derived after subtraction of image b from image c
importantly, the threshold identifies lung as air. The way to segment air is to do a region labeling algorithm that identifies each group of pixels of equal value. Then, the number of pixels in each labeled region is calculated using a histogram algorithm. The small regions can be cut by cutting all regions of a size below a certain threshold. Since air and tissue are the largest regions, all cuts smaller than these two regions are cut leaving the air segmentation in figure 3.4c. The lungs can then be segmented by subtracting figure 3.4b from figure 3.4c to obtain the lung segmentation shown in figure 3.4d. Once the air and lungs are segmented, all other pixels are assumed to be soft tissue. Bone is by far the hardest to segment in MRI. No adequate MRI sequence or segmentation has yet been developed for bone segmentation in MRI. In order to sidestep this problem, segmentation of bone will be done by using a threshold on CT, and the result will be overlapped onto the registered MRI. This method requires a CT and cannot be used in practice. It is presented here for calculations of the effectiveness of the inclusion of bone in the segmentation. 46
Figure 3.5: Axial chest slices for (Left) original MRI image with ROIs drawn for air, lung, tissue, and spine; (Right) 3 region segmentation of air, lung, and tissue and 4 region segmentation of air, lung, tissue, and bone using the assigned values
Once the regions of air, lung, bone, and soft tissue are formed, the attenuation values for the specific tissue group are assigned to each region. An attenuation value of 0.002 cm−1 is assigned to air, 0.030 cm−1 to lung, 0.098 cm−1 to soft tissue, and 0.130 cm−1 to bone. The attenuation values are derived from averaging the regions in the CT attenuation maps for all five beagles as shown in figure 3.5. Regions of interest (ROI) are drawn on the CT map for several slices for all the beagles to obtain an average attenuation for each region. In this study two attenuation maps are of interest. One map, called a 3 region map, consists of segmenting only air, lung, and other tissues. The other map, called a 4 region map, is a segmentation of air, lung, soft tissue, and bone.
3.5 3.5.1
PET Reconstruction Background
The two main methods for reconstruction of PET images is filtered back projection (FBP)[53] and ordered subsets expectation maximum (OSEM)[54]. FBP traces the 47
Figure 3.6: One dimensional projection of 2D slice of an object[55]
lines of response from the detector listmode data and filters high and low frequencies in Fourier Space. OSEM involves estimating the likelihood that an annihilation has occurred along a certain line of response in the data. FBP has the advantage of being simple and fast computationally, but OSEM is more commonly used for clinical scanners due to reduced noise in the image. FBP involves filtering the sinogram data before back projection. A backprojection is simply the reconstruction of the image from the projection data by summing all the lines of response. The data is the collection of 1D projections of all angles of the 2D slice of the object as shown in figure 3.6. This transformation from the object to a series of one dimensional projections is known as a Radon transform. The Radon transform represents the actual projections from the data collected in PET systems. The data from these systems are collected in a series of sinograms that are later used to reconstruct the data. A line of response is stored as a single point in the sinogram, whose location depends on the angle, φ, and position, xr of the projection. The output of the scanner is in the sinogram format, and a simple backprojection 48
Figure 3.7: Commonly applied filters to Fourier transformed images used for filtered backprojection for PET reconstruction[56]
would reveal the image. However, due to excessive noise and blurring, the data needs to be filtered first. Filtering is done by applying a filter to the 2D Fourier transform of the sinogram data. The filters used are high pass filters which remove the blurring effect from line integration. However, the filters allow high frequency noise which can diminish the quality of the image. High frequency noise comes from the randomness of the detection of coincidences. That is, a point source will not result in an even distribution of counts around the detector ring due to randomness in the decay effects. Some common filters used are in figure 3.7. These filters are used by multiplying the data at each frequency by the gain of the filter at that frequency. Traditionally, ramp figures are used to filter the data. Since the ramp filters allow high frequency noise in the image, other filters, like the Hann and Hamming filters, 49
Figure 3.8: Comparison of reconstruction using (top) FBP and (bottom) OSEM[57]
are used to cut out both the high and low frequency noise. No matter which filter is used, FBP will produce streaking artifacts as shown in figure 3.8, which is why it is now an obsolete reconstruction method. The advantage of FBP is that it is a quick and simple algorithm, but due to the advancement in computer technology, FBP is rarely used. The way to produce the highest quality images is through OSEM[58]. OSEM is based on the expectation maximum algorithm, which estimates the image from the probability that an emission from a certain pixel is recorded in a certain LOR. OSEM is a special expectation maximum algorithm that involves dividing the original data into ordered subsets that apply the expectation maximum algorithm to each of the subsets in turn. The OSEM algorithm first uses an initial estimation of the image, usually FBP and reprojects the result. The projections are compared to
50
the original projections to test the accuracy of the estimate. The mean difference between the projection data and initial estimate is calculated in order to make a new estimate of the image. The new image is reprojected and compared to the original projection data. The process continues with iterations until there is a reasonable level of convergence between the estimated image projections and the original projections. OSEM speeds up the process by creating a number of ordered subsets. Each subset must be equal in size and symmetrical in the image. For example, for four subsets, one subset would be for projections at angles of 0 to 45 degrees, a second projection at angles of 45 to 90 degrees, and so on. The resulting reconstruction using one subset becomes the starting value for the next subset. Each iteration involves a number of subiterations equal to the number of subsets. The image quality of the OSEM algorithm with n subsets and one iteration is approximately equal to that of 1 subset and n iterations. Also, one full iteration with any number of subsets will have almost the same computation time as one iteration with 1 subset. So, if 8 subsets are used, OSEM will be approximately 8 times faster with nearly the same image quality than if only 1 subset is used. The theoretical limit to the number of subsets is the number of projections in the system, but a number of subsets less than the number of projections are often used due to convergence problems. The appropriate number of subsets is determined by the attenuation density, subset balance, and level of noise in projection data. A comparison between FBP and OSEM is given in figure 3.8. The images show a pair of slices from the torso of a patient using both FBP and OSEM algorithms. The OSEM algorithm gives a clean image with no streaking artifacts.
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3.5.2
Implementation
Reconstruction of the PET data in this experiment was done using the freely available Software for Tomographic Image Reconstruction(STIR). The software package provides tools for normalization, rebinning, and reconstruction. The three input files needed to reconstruct the PET data are the raw data, which contain the sinograms, the normalization file, and the attenuation file. The attenuation is calculated either from the CT or the MRI. The output of the PET/CT gives PET and CT data with an equal number of slices and equal matrix size that are fully registered. The CT data must be converted from Hounsfield units to attenuation (cm−1 ) as discussed in section 2.3.2. The MRI attenuation maps are generated by the registration and segmentation process resulting in figure 3.5. Both the CT and MRI attenuation images need to be split up into 6-7 data files, one for each bed, since the raw data for each bed is given. The data must be separated into these files with equivalent overlap to the PET parameters discussed in section 3.1 (81 slices, 162 mm length, and 40 mm overlap). Then, the attenuation correction factors were obtained by forward projecting the attenuation map and dividing by the normalization to complete the correction factors. Once the normalization and attenuation correction are calculated, the correction data and PET image data are both rebinned using Fourier rebinning (FORE)[59]. Rebinning involves treating 3D events as a series of 2D events. This way the data can be reconstructed with 2D methods such as FBP and OSEM. Rebinning methods essentially approximate oblique planes as a series of direct planes such as in figure 3.9. In the figure a coincidence is backprojected correctly and the rebinning algorithm approximates the event as a series of parallel and independent two dimensional data 52
Figure 3.9: (A) A coincident photon pair at an oblique angle; (B) Backprojection approximated as a series of parallel two dimensional coincidence events[60]
instead of a direct line from one detector to the other. Fourier rebinning(FORE) is a type of rebinning that uses the frequency distance relationship to approximate oblique data as two dimensional. Given a 3D sinogram, one can do a first order approximation of the Fourier transform of the oblique sinogram to achieve a series of 2D sinograms. The equation derived by Defrise et al[59] is P (ω, k, z, δ) ≈ P (ω, k, z −
kδ , 0) ω
(3.1)
where ω is the transaxial frequency, k is the Fourier index, and z is the axial position. Also, δ = tan(θ) where θ is the angle between the lines of response and the transaxial plane. The approximation only needs interpolation in the axial, z, direction and does not require interpolation of the frequency variables ω and k. After normalization, attenuation correction, and rebinning, the data can be reconstructed using OSEM. The data was reconstructed with 8 subsets and 2 iterations. This was chosen due to the high level of convergence after 2 iterations. Using 3 or 53
even 4 iterations does not noticeably improve the quality of the image, so 2 iterations is sufficient. The result of the reconstruction is the fully normalized and attenuation corrected PET image.
3.6
Analysis
In an effort to quantify the accuracy of the MRI attenuated versus the CT attenuated maps, a couple methods were used. One method is to calculate the standardized uptake values (SUV) of manually selected regions of interest (ROI) and compare the results among the different attenuation maps. The other method is to take the mean relative difference (MRD) of images reconstructed from different attenuation maps. These methods for quantifying the accuracy were used in conjunction with visual assessments of the resulting images. The first method for analyzing the data was to draw a region of interest around the lungs, heart, liver, and spine. In the case of the spine, the ROI was drawn from the registered CT attenuation image. Figure 3.10 shows an example of the ROI drawn for lung, heart and spine. After the ROI was drawn, the standardized uptake values[61] (SUV) were averaged over the entire region. The SUV of the ROI was calculated by SU V =
T issue Concentration(Bq/mL) Injected Dose(Bq)/P atient0 s W eight(g)
(3.2)
where the tissue concentration is calculated by T issue Concentration =
P ixel Intensity e−ln(2)∗t/t1/2
(3.3)
where t is the time after injection, t1/2 is the half life of F18 , and pixel intensity has been rescaled to achieve concentrations in Bq/mL. This same method was used for calculating the SUV of an ROI for a dynamically acquired scan. By doing this one 54
Figure 3.10: ROI of the lung, heart, and spine for a CT attenuated PET slice of the chest of a beagle
can see how the SUV varies in time for PET images reconstructed from different attenuation maps. Another method for comparing the differences between the images is to calculate the differences on a pixel by pixel basis. This involves calculating the mean relative difference using the equation, M RD =
N 1 X |P1,i − P2,i | N i=0 (P1,i + P2,i )/2
(3.4)
where i is the pixel number, N is the total number of pixels, P1,i is the value at pixel i of the first image, and P2,i is the value at pixel i of the second image. The mean relative difference between any two images can be calculated using this equation. However, for cases where the pixel intensity at a certain location for both images is zero, the denominator is equal to zero and the pixels were excluded from the average.
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CHAPTER 4
RESULTS AND DISCUSSION
4.1
Beagle Images
The weight of each of the five beagles along with the injected dose is in Table 4.1. Each beagle underwent a 60 minute dynamic scan, and injection of FDG took place one minute after the start of the scan. That scan was immediately followed by a 6-7 bed, full body scan taken approximately 60 minutes after injection. The MRI images were segmented using both 3 region and 4 region maps and used for reconstruction of the PET data. CT based attenuation is also used to reconstruct the PET and is compared to the MRI based attenuation.
Beagles 1 2 3 4 5
Weight 6.60 kg 13.2 kg 11.2 kg 12.0 kg 12.6 kg
Dose 2.70 mCi 3.64 mCi 3.54 mCi 3.50 mCi 3.43 mCi
Table 4.1: The weight and dose for each beagle
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Figure 4.1: CT topogram indicating the head, neck, chest, and hind slices used in figure 4.2
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Figure 4.2: From left to right, PET slices of the head, neck, chest, and hind are shown (see figure 4.1). From top to bottom, the PET was reconstructed using no attenuation, MRI 3 region attenuation, MRI 4 region attenuation, and CT attenuation. The axial slices refer to the red lines drawn in figure 4.1.
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Four slices of the beagle for each of four different attenuation maps is shown in figure 4.2. None refers to no attenuation, MR1 refers to a 3 region segmentation, MR2 refers to a 4 region segmentation, and CT refers to CT attenuation. In these images, the unattenuated PET images are much worse than both the CT and the MRI attenuated PET images. MR1 and MR2 are indistinguishable from each other despite the difference in attenuation maps. Also, both the MRI attenuated PET images are very similar to the CT attenuated images, and there are significant differences only in places where misregistration occurred. Figure 4.3 shows examples of the resulting PET images from three different attenuation maps along with the corresponding attenuation maps. As can be seen, even with minor registration errors, the MRI based attenuation seems accurate. However, further calculation reveals some differences between the images.
4.2
Standardized Uptake Values for Various Regions
Figures 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, and 4.10 show the SUVs for the heart, liver, lung, spine, brain, kidney, and bladder respectively averaged for all five beagles using three different attenuation methods along with no attenuation. Table 4.2 lists the values from the graph in a table. Looking at both the figure and table, it is clear that the average SUVs using the CT, MR1, and MR2 methods are very similar. However, the average SUVs without attenuation are quite different from the CT and MRI based attenuation corrections. The percent difference between CT and MRI based attenuations and between CT and no attenuation are shown in table 4.3. According to the table there is only a 1-10% difference in average SUV, with the highest percent difference between the
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Figure 4.3: Above shows a chest slice of a (a) CT attenuation map, (b) MRI 3 region attenuation map, and (c) MRI 4 region attenuation map. These attenuation maps were used to reconstruct the (d) CT attenuated PET images, (e) MRI 3 region attenuated PET images, and (f) MRI 4 region attenuated PET images. The red arrows indicate a narrowing in the separation of the lungs due to misregistration, and the blue arrows indicate small changes in pixel intensity values in the spine due to the differing segmentations.
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SUV (g/ml) Heart Liver Lung Bladder Brain Kidney Spine
CT MR1 MR2 None 5.5 5.2 5.4 5.3 2.4 2.3 2.3 1.8 0.8 0.8 0.8 1.1 138 147 147 162 5.4 5.7 5.7 5.3 9.7 9.8 9.5 8.2 1.8 1.6 1.8 1.3
Table 4.2: Standardized Uptake Values (SUV) for CT attenuated PET (CT), MRI 3 region attenuated PET (MR1), MRI 4 region attenuated PET (MR2), and unattenuated PET (None)
Figure 4.4: Average SUV of the heart for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None)
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Figure 4.5: Average SUV of the liver for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None)
CT and 3 region MRI attenuation in the SUV of the spine. This is of particular interest since the only difference between the 3 region and 4 region MRI attenuations is segmentation of the bone. It is clear that additional segmentation of the spine resulted in a more accurate estimate of the SUV (3% vs 10%). However, 10% is a low enough percentage that in most cases would not be visually apparent in the images. Therefore, using a 3 region segmentation is sufficient, but researchers who calculate SUVs in the spine, like in the case of spinal tumors, should take into consideration that a 10% lower SUV is expected. In the case of the other organs, the SUVs vary from beagle to beagle such that the CT attenuated PET images are sometimes greater than and sometimes lower than the SUVs of the 3 region MRI attenuated PET images. However, in the spine the SUV is consistently lower in the 3 region MRI attenuated 62
Figure 4.6: Average SUV of the lungs for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None)
images, so one can make the assumption that when using a 3 region map, lower SUVs in the spine are expected. Table 4.3 also shows the difference between using CT attenuation and no attenuation. In that case there is a 1-37% difference in SUV. The biggest difference is in the lungs where there is a 37% difference. Also, there is a large percent difference in the spine, 29%, which is much greater than the 10% found by using a simple 3 region segmentation. Thus, a 3 region segmentation shows a vast improvement over no attenuation, and a 4 region segmentation only produces slightly better results than the 3 region segmentation. It is doubtful that additional segmentation would be necessary since 4 region segmentations differ from CT based attenuation by only 1-6%. 63
Figure 4.7: Average SUV of the spine for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None)
Percent
CT vs CT vs MR1 vs CT vs MR1 vs MR2 vs MR1 MR2 MR2 None None None Heart 5% 1% 4% 3% 2% 1% Liver 6% 4% 2% 29% 21% 23% Lung 2% 3% 1% 37% 42% 41% Bladder 6% 6% 0.1% 16% 10% 10% Brain 5% 6% 1% 1% 6% 7% Kidney 1% 2% 3% 17% 16% 14% Spine 10% 3% 12% 29% 18% 28%
Table 4.3: Percent difference between CT attenuated PET SUVs and MRI attenuated PET SUVs
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Figure 4.8: Average SUV of the brain for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None)
4.3
Mean Relative Differences
Figures 4.11, 4.12, 4.13, 4.14, 4.15, 4.16, and 4.17 show the mean relative differences (MRD) for ROIs of the heart, liver, lung, spine, brain, kidney, and bladder respectively averaged for all five beagles. Table 4.4 lists the values from the graph in a table. When comparing CT attenuated PET images to the MRI attenuated images, the MRD is in the range of 5-9% for all regions excluding the spine. In the spinal region, the CT to MR1 MRD is 14% and the CT to MR2 MRD is 6%. Thus, without segmentation of the spine in MR1, a noticeably higher MRD is calculated. However, the MRD of the CT attenuated PET images to the unattenuated PET images is in the range of 10-45%. In particular the spinal region had a 45% MRD, which is
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Figure 4.9: Average SUV of the kidney for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None)
much greater than the 14% MRD found comparing the CT to 3 region segmentation. Similarly, the MRD between the unattenuated PET images and the MRI attenuated PET images for both 3 region and 4 region segmentations was in the range of 18-52%. Therefore, the 3 region segmentation is a vast improvement over no attenuation, and a 4 region segmentation only improves the quality slightly in the spinal region.
4.4
Dynamic Scan
A dynamic scan of the beagle consisting of 60 scans of 1 minute of duration was done. The beagle was injected with FDG one minute after the beginning of the scan. Figure 4.18 shows the graph of the average SUVs for the heart, lung, kidney, and liver for both CT and MRI attenuated PET scans. The SUVs in each case follow the exact 66
Figure 4.10: Average SUV of the bladder for all the beagles for (1) CT attenuated PET (CT); (2) MRI 3 region attenuated PET (MR1); (3) MRI 4 region attenuated PET (MR2); (4) No attenuation (None)
Percent
CT vs CT vs MR1 vs CT vs MR1 vs MR2 vs MR1 MR2 MR2 None None None Heart 6% 5% 2% 14% 19% 18% Liver 7% 7% 2% 44% 43% 42% Lung 8% 7% 4% 37% 27% 30% Bladder 5% 5% 3% 23% 43% 52% Brain 6% 7% 1% 10% 33% 32% Kidney 9% 8% 2% 16% 26% 27% Spine 14% 6% 13% 45% 30% 31%
Table 4.4: Mean Relative Differences
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Figure 4.11: The MRD of the heart between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none
same pattern with a slightly lower SUV (3%) in the MRI attenuated PET with the exception of the spine (20%). The SUV is consistent with the average pixel intensity measurements taken earlier that indicated a 2% - 5% difference in value for the heart, liver, and lung, and a much higher difference for the spine. This indicates that SUV measurements of a PET image using an approximate attenuation map stays relatively constant to the CT attenuated PET as a function of time. SUV time independence is important because in this particular study, the scan was taken 60 minutes after injection of 18-F FDG. Other hospitals and universities take PET scans 45, 75, or 90 minutes after injection. Since the dynamic scan demonstrates
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Figure 4.12: The MRD of the liver between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none
SUV time independence, the differences in SUV averages calculated in tables 4.3 and 4.4 would be the same at any scan time.
4.5
Discussion
The results show that there is a small, but noticeable difference between using attenuation images with or without bone. Figure 4.3 shows SUVs of the spine 10% lower on average for the 3 region attenuation compared to the CT attenuation, and a 3% difference in SUV for the 4 region segmentation. Also, the difference in SUV averages between the CT and the MRI segmented PET was only 1-6%. Since the SUVs for both MRI attenuated PET images are about the same, differences in the attenuation maps of the spinal region do not have a noticeable effect on surrounding 69
Figure 4.13: The MRD of the lung between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none
tissues. The SUV difference between the CT attenuated and unattenuated PET is as high as 37% in the lungs with large differences of 29% in the spine and liver. Thus a 10% difference in the SUV of the spine is not that high relative to the unattenuated images, and it is not visually obvious in the images as it is with the unattenuated images. The differences in SUV were explored in more detail by introducing the MRD calculation. The MRD between the CT and the 4 region segmented PET for the spinal region was 6% and between the CT and the 3 region segmented PET was 14%. For the heart, liver, and lung, the MRD between CT and the 3 region segmented PET was 6-8%, and between CT and the 4 region segmented PET was 5-7%. The MRD confirms the results of the SUV average calculation that a 3 region segmentation is 70
Figure 4.14: The MRD of the spine between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none
highly accurate for most regions, and is only slightly less accurate in the spinal region. When compared to the 10-45% MRD between the CT attenuated and unattenuated PET images, the MRI based attenuation corrections are highly accurate. Segmenting the spine might only be necessary for quantitative calculations in the spine, such as finding the SUV of a spinal tumor. However, for qualitative assessments the difference between the 3 region segmented PET and the 4 region segmented PET is not noticeably different (see figure 4.3). Visual differences between the CT attenuated PET and the MRI attenuated PET are primarily due to misregistration in the image, which could easily be avoided by scanning the beagle on a single bed, such as in a combined MRI/PET.
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Figure 4.15: The MRD of the brain between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none
As has previously been shown, the attenuation is essential for both high quality PET images and accurate SUV calculations. Small changes to the attenuation map can result in noticeable visual and quantitative changes in the final PET image. Thus, it is essential to achieve appropriate attenuation images when performing the reconstruction. However, in the case of whole body MRI based attenuation, only an approximate attenuation map derived from visually distinguishable tissues of the images is possible. It is neither feasible nor practical to derive attenuation images from MRI to be equivalent to the CT attenuation, so an approximate attenuation image must be generated. The most important question to ask is what tissue areas of the body have the largest difference in attenuation to other areas. For example, the heart, liver, and kidneys all have approximately the same attenuation value, so 72
Figure 4.16: The MRD of the kidney between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none
assigning each one a different attenuation value will not produce noticeably higher quality PET images than assigning a single attenuation value for all three. On the other hand, the lungs have much lower attenuation than the heart, so it is necessary to assign the lungs a different attenuation value than the other tissues in the body. Thus, segmenting and assigning attenuation values to each tissue region of the body is not necessary. One example of the segmentation of tissue regions for the purpose of deriving attenuation maps was discussed in a paper by Zaidi et al[37]. In the paper Zaidi demonstrated an automated segmentation algorithm for the head that involved segmenting regions of air, skull, nasal cavities, and other tissues. The segmented tissue regions were then assigned their corresponding attenuation values and were used for 73
Figure 4.17: The MRD of the bladder between reconstructed PET using these attenuations: (1) CT and 3 region MRI; (2) CT and 4 region MRI; (3) 3 region MR and 4 region MRI; (4) CT to none; (5) 3 region MRI and none; (6) 4 region MRI and none
PET reconstruction. In the paper a single attenuation value was assigned to the brain, scalp, and intercranial fluids due to their relative similarity in attenuation value. The result of his work was high quality PET images of the brain that were nearly indistinguishable to PET images derived from transmission based attenuation maps. Just like in this work, regions of similar attenuation values were grouped together and assigned identical attenuation values. However, unlike in this work, automated segmentation of the bone was not performed. The head is a fairly consistent elliptical shape which made segmentation of the bone relatively easy. In the chest and abdomen, segmentation of the spine and ribs is much more difficult and much less practical. Thus, approximating the attenuation of bone to be like that of the surrounding tissue was performed. Surprisingly, SUV calculations of the brain reveal 74
Figure 4.18: CT attenuated (red) and MRI 3 region attenuated (blue) dynamic PET scans for the heart, lung, liver, and spine. Each graph shows the SUV for each region versus the time in minutes from the beginning of the scan from 0 to 60 minutes.
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only a 5% difference between the CT attenuated PET and MRI 3 region attenuated PET, and only a 6% difference between the CT attenuated PET and MRI 4 region attenuated PET. Moreover, the MRD calculations of the brains show only a 6% difference between the CT attenuated PET and MRI 3 region attenuated PET, and only a 7% difference between the CT attenuated PET and MRI 4 region attenuated PET. Therefore, segmentation of the skull in brain imaging may not be necessary either. Before CT was used for attenuation correction, germanium-68 transmission scans were the gold standard[62]. As can be seen in figures 2.14 and 2.9, the transmission scans do not make visually noticeable distinctions between different organ groups. The most visually apparent regions are the regions of air, lung, and other tissue. It may be reasonable to assume that nearly all tissues outside of the lungs have approximately the same attenuation value, so there is no need to segment further. This was the main inspiration toward the development of a three region segmented MRI for attenuation corrections in PET imaging. The data suggests that a three region segmented MRI is an accurate attenuation map and the resulting PET images are extremely similar in appearance and uptake values to that of a CT attenuated PET image.
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CHAPTER 5
CONCLUSION
A three region segmentation of a full body MRI scan is a good approximation for an attenuation map for PET reconstruction, and it does not differ much from the CT attenuated PET images. This result shows that small changes in attenuation between various tissues, such as in the heart, liver, and brain, do not greatly affect the resulting reconstruction. So, it is reasonable to assign a single attenuation value to fat, muscles, and even bone. However, it was shown that assigning a higher attenuation value to bone in a four region segmentation does improve the accuracy of uptake values in the bone, but for tissues outside of the bone, there does not seem to be any significant qualitative or quantitative differences. Due to the need to acquire MRI attenuation maps quickly alongside with the desired MRI images, a fast acquisition as well as a fast segmentation is needed. In this research it was found that a very low repetition time on the order of a few milliseconds produced MRI images that are the easiest to segment. A single slice of this MRI attenuation scan can be acquired in a fraction of a second. However, segmentation of the bone would likely require additional scans and longer computation times, so it would not be practical for fast acquisitions. Therefore, a three region segmentation, without segmentation of the bone, is a fast, practical solution to attenuation correction in MRI/PET hybrid systems. 77
5.1
Further Research
The combination of MRI and PET into a single scanner has many challenges that have yet to be solved. On the hardware side combining the two scanners to allow simultaneous acquisition requires PET hardware unaffected by strong magnetic fields that are small enough to fit inside the bore of the MRI magnet. On the software side data is acquired simultaneously, so both sets of data need to be integrated. This sets MRI/PET apart from other dual modality systems such as SPECT/CT and PET/CT, which acquire data sequentially. From a PET attenuation correction perspective, one could register each MRI scan to the PET scan as it is acquiring. Thus, multiple attenuation images would be necessary for a single PET scan due to motion caused by respiration, peristalsis, and cardiac contractions. This would be challenging from a technical standpoint since MRI and PET are acquired and reconstructed in a very different way. The MRI can collect time data easily since it scans each slice sequentially. PET acquires all slices simultaneously into a single bed, so the acquisition would have to be separated into multiple time points and coordinated with the MRI scan. However, simultaneous acquisition has the advantage of reducing motion artifacts in the lungs and other moving parts. This could be used in practice to identify small lesions in the lung that would otherwise go unnoticed due to the motion blur caused in PET imaging. The research potential in MRI/PET systems is vast with multiple research facilities around the world. There are already several prototype systems that have produced preliminary scans. The attenuation procedure method outlined in this paper can be applied to these prototype systems to provide more insight into the accuracy of the method without the problems of misregistration. Overall, there is much room 78
for improvement in attenuation correction and these methods will be further explored with currently available prototype systems.
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