Fully automated intrinsic respiratory and cardiac gating for small animal CT
J Kuntz1, J Dinkel2, S Zwick3, T Bäuerle1, M Grasruck4, F Kiessling5, R Gupta6, W Semmler1 and S H Bartling1
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Dept. of Medical Physics in Radiology, German Cancer Research Center, Heidelberg,
Germany 2
Dept. of Radiology, German Cancer Research Center, Heidelberg, Germany
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Dept. of Diagnostic Radiology, Medical Physics, Freiburg University, Germany
4
Siemens Healthcare, Forchheim, Germany
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Dept. of Experimental Molecular Imaging, RWTH-Aachen University, Medical Faculty,
Aachen, Germany 6
Dept. of Radiology, Massachusetts General Hospital, Boston, MA, USA
Corresponding author: Dipl. Ing. (FH) Jan Kuntz Address: Department of Medical Physics in Radiology (E020) German Cancer Research Center (DKFZ) Im Neuenheimer Feld 280 69120 Heidelberg Germany Email:
[email protected] Phone: + 49 6221 42 2686 Fax: +49 6221 42 2613
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Abstract. A fully automated, intrinsic gating algorithm for small animal cone-beam CT is described and evaluated. A motion parameter, derived from the raw projection images, is used for both cardiac and respiratory gating. The proposed algorithm makes it possible to reconstruct motion-corrected still images as well as to generate four-dimensional (4D) data sets representing the cardiac and pulmonary anatomy of free-breathing animals without the use of electrocardiogram (ECG) or respiratory sensors. Variation analysis of projections from several rotations is used to place a region of interest (ROI) on the diaphragm. The ROI is cranially extended to include the heart. The center of mass (COM) variation within this ROI, the filtered frequency response, and the local maxima are used to derive a binary motion-gating parameter for phase-sensitive gated reconstruction. This algorithm was implemented on a flat-panel based cone-beam CT scanner and evaluated using a moving phantom and animal scans (7 rats and 8 mice). In all cases it resulted in robust gating signals. The maximum volume error in phantom studies was less than 6%. By utilizing extrinsic gating via externally placed cardiac and respiratory sensors, the functional parameters (e.g. cardiac ejection fraction) and image quality were equivalent to this current gold standard. This algorithm obviates the necessity of both gating hardware and user interaction. The simplicity of the proposed algorithm should enable its adoption in a wide range of small animal cone-beam CT scanners.
Keywords: Automatic intrinsic gating, self-gating, small animal imaging, CT reconstruction, flat-panel cone-beam CT, respiratory and cardiac gating Classification: 87.57.Q- Computed tomography
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1. Introduction Small animal CT is increasingly being used in biological and preclinical research to non-invasively study many diseases and experimental models of diseases (Bartling et al 2007b, Ritmann 2004, Seo et al 2008, Liang et al 2008). Among them are experimental disease models relating to the lung and heart (Kiessling et al 2004, Detombe et al 2008). Lung imaging is crucially dependent on image quality, for example, for tracking of subtle changes in the parenchyma or for detecting small metastatic foci. Similarly, high image quality is necessary to adequately visualize and evaluate the coronary arteries and cardiac structures in small animals. Both the lung and the heart are affected by physiological motion that is present during scanning (Drangova et al 2007, Dinkel et al 2008). Such motion can severely compromise image quality if an appropriate gating method for image reconstruction is not used. Gating methods are also used to reconstruct different phases of the moving organ to enable four-dimensional (4D) displays of the moving organ (Detombe et al 2008), making it possible to derive functional data (Drangova et al 2007, Bartling et al 2007a). Many gating methods have been implemented in small animal CT (Chavarrias et al 2008). Prospective gating (Cavanaugh et al 2004) relies on the acquisition of X-ray projections at the appropriate phase during the cardiac or respiratory cycle. Retrospective gating for cone-beam CT is being used more and more frequently (Drangova et al 2007, Bartling et al 2007a, Chavarrias et al 2008, Sera et al 2008, Ertel et al 2009). Typically, each projection is tagged using an external gating trigger reflective of the cardiac and/or respiratory phase. The projections are then retrospectively binned according to the phase they reflect, and projection data representing a given phase is reconstructed. This approach which relies on an external trigger to derive a gating signal (Bartling et al 2007a), however, increases the preparatory effort because of the placement of external leads, is somewhat unreliable, and is prone to artifacts and image quality degradation. To overcome these limitations, several authors have recently proposed intrinsic gating methods that derive the gating signal from the projection itself (Farncombe 2008, Bartling et al 2008, Hu et al 2004, Ertel et al 2009). Using these methods, no additional sensor is necessary to acquire a gating signal. Compared with the non-gated images, these newer intrinsic gating schemes are able to achieve
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improvements in image quality similar to that in extrinsic gating schemes, and it is possible to derive functional data (Bartling et al 2008). To date, most of the published intrinsic gating methods have been limited to respiratory gating with only one published algorithm that pertains to cardiac gating (Dinkel et al 2008). The majority of the published literature has used continuously acquired projection data from multiple rotations around the animal (Farncombe 2008, Bartling et al 2008, Ertel et al 2009). Angular step-and-shoot techniques, in which projections for the whole respiratory phase are acquired at one angular position before the gantry is moved to the next position (Chavarrias et al 2008, Hu et al 2004), have also been described. Common to all intrinsic gating methods is a motion parameter that is derived from the projection data. Retrospective resorting of projections is performed on the basis of either the phase (Bartling et al 2008, Dinkel et al 2008, Ertel et al 2009) or the amplitude (Chavarrias et al 2008, Farncombe 2008, Hu et al 2004) of the computed motion parameter. In some methods, manual interaction is necessary to define a region of interest (ROI) that encloses at least parts of the diaphragm (Farncombe 2008, Bartling et al 2008, Hu et al 2004, Ertel et al 2009). The center of mass (COM) within the ROI (Bartling et al 2008), the median within the ROI, the mean pixel intensities (Farncombe 2008, Hu et al 2004), and the skewness of the histogram of difference images (Chavarrias et al 2008) have all been used to calculate the motion parameter. Filtering is used to increase the effect of the respiratory motion by suppressing the effect of angular position on the parameter (Farncombe 2008, Bartling et al 2008, Dinkel et al 2008). The algorithms were tested in mice (Farncombe 2008, Bartling et al 2008, Dinkel et al 2008), rats (Chavarrias et al 2008, Farncombe 2008, Bartling et al 2008, Hu et al 2004, Dinkel et al 2008) and rabbits (Bartling et al 2008). The key advance in the algorithm proposed in this paper for small animal CT is its fully automatic nature. It requires no user interaction, not even placement of a preliminary ROI. In mice and rats, where this algorithm was tested, it works reliably for both cardiac and respiratory gating.
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2. Materials and methods 2.1. The proposed fully automated gating algorithm The proposed gating algorithm is suitable for a small animal cone-beam CT scanner such as a flatpanel based system because sufficiently large parts of the lung, upper abdomen and heart have to be covered by each projection. This algorithm is suitable for a continuously rotating CT scanner and is applied retrospectively. Besides the raw projection data, information about the species and the orientation of the scanned animal is necessary. Scanner-specific corrections such as air calibration and defective pixel map correction should be applied to the raw projection data before the algorithm is invoked. No additional hardware is necessary either to mark the animal or to derive any cardiac or respiratory signal. The fully automated intrinsic gating algorithm works as follows: •
A ROI is automatically positioned with respect to the motion during the scan using variation analysis and sinusoidal fit.
•
From this ROI, motion parameters are calculated and filtered. Characteristics of these filtered motion parameters are used to create cardiac and respiratory gating signals.
•
The derived gating signals are used to resort the projections with respect to relative phase.
A flow chart of the proposed algorithm is shown in (Fig. 1). Each step is described in detail in the following sections. 2.1.1 Automated positioning of ROI The aim of this step is to find a ROI within the projection data that covers the heart and the diaphragm by a variation analysis between the projections acquired at the same angular position but from different rotations and hence from different time points. The ROI position was approximated by using a sinusoidal fit. Variation analysis A boxcar filter is applied to the projection data first (Fig. 2a). As a result, for each pixel a new value, representing the local COM in the z direction, is derived from a 9 x 9 pixel neighborhood (Fig. 2c). This step is required because projections from several rotations are acquired from slightly different angular positions in our scanner. This inaccuracy leads to a relative xy shift of structures between
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different projections that are nominally from the same angular position. Variation analysis alone results in large spurious signal change, mainly from edges that are oriented along the z direction and are very far from the isocenter. These spurious difference signals compromise the appropriate ROI positioning (Fig. 2b). The boxcar filter eliminates these misleading signals, while the signal from the movement of the diaphragm in the z direction is not affected. Additionally, the boxcar filter provides low-pass filtering, which reduces image noise. Using these filtered projections, variation analysis is performed on the basis of the sum of the squared differences (SSD):
where Xi and Xj are projection matrices at nominally the same angular position from n rotations. Due to the respiratory movement, both extreme positions of the diaphragm caused large values within the difference image (Fig. 2d). However, only the upper position of the diaphragm, being closest to the average heart position, was assumed to be relevant for the ROI definition. Therefore the center of the ROI was defined as the median of the highest, most cranially located pixels. The size of the ROI was predefined according to the size of the respective species, i.e. for mice 50 x 3 mm² in the isocenter (250 x 15 pixel) and for rats 75 x 7 mm² (375 x 35 pixel). From the diaphragm, the ROI was extended in cranial direction to include the heart (Fig. 3). The extension was 2 mm (10 pixels) for mice and 3 mm (15 pixels) for rats. Sinusoidal fit Ideally, the xy position of the ROI would follow a sinusoidal curve with respect to the angular position, similar to the progression of the sinogram. However, in practice, the calculated ROI positions were compromised by noise and single outliers. These erroneous overlays were eliminated by filtering in the frequency domain, where all channels beside the offset and the fundamental mode were reset. The inverse Fourier transformation resulted in an ideal sinusoidal curve representing the ROI position corresponding to each projection angle. The movement of the ROI position in the z direction also follows a sinusoidal curve, but the magnitude is rather small when the object is close to the center slice, so that the position in z direction was determined by averaging.
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2.1.3 Calculation of gating signals The COM within the ROI was calculated as a motion parameter. The logarithm of the raw projection data for each pixel was first computed to linearize it according to the Beer-Lambert law. For the COM calculation, each line sum of projection values (mz) was multiplied by a weighting factor equal to its z position within the ROI. Weighted projection values from all the lines were summed and divided by the total sum of projection values from the ROI (Dinkel et al 2008): where mz is the sum of pixel values of line z and Z a weighting factor identical to the z coordinate. A high-pass filter was used to suppress frequencies up to 1 Hz as well as the offset. Binary respiratory gating information was extracted from the filtered COM signal by using upper limit detection. The upper limit was defined as the value that is three times the standard deviation. All values that exceeded this limit were set to high level in the binary gating signal. The impact of cardiac motion on the COM signal was much lower than that of respiratory motion (Fig. 4a). It was still possible to extract the cardiac gating signal by using a precisely trimmed band pass filter with cut-off frequencies of 3 and 9 Hz, respectively, representing heart rates of 180 to 540 min-1 (Fig. 4b). To nullify the phase shift, a bidirectional Hamming-window finite impulse response (FIR) filter with an order of 100 was used. Using local maxima detection, the filtered motion signal were converted into a binary cardiac gating signal. 2.1.4 Motion-gated reconstructions To realize motion-gated reconstructions, projections from 16 rotations were retrospectively resorted to a new motion-compensated dataset representing one virtual rotation. The resorting was achieved with the help of the gating signal. Each gating event was defined as the 0% position of the motion cycle. For respiratory gating, projection data between 20% and 80% of the respiratory cycle were used. For cardiac gating, the heart cycle was divided into five segments, each containing 20% of all projections, resulting in a four-dimensional data set (Fig. 5). To simultaneously perform respiratory and cardiac gating only those projections that complied with both gating criteria were used. The resulting dataset consisted of projections with inconsistent angular steps and potentially blank projections due to gating. Using linearly weighted interpolation a new dataset consisting of 600
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equidistant projections was calculated. This conversion allowed image reconstruction using conventional cone beam reconstruction software.
2.2. Validation The proposed gating algorithm was tested using a flat-panel based volume-CT system. A moving phantom was used to validate the gating algorithm and verify the consistency of volume measurements. Datasets of free-breathing mice and rats were reconstructed using this fully automated intrinsically gating algorithm. Image quality and functional parameters were compared between automated intrinsic gating and extrinsic gating as the gold standard (performed with the help of ECG leads and respiratory cushion). 2.2.1 Cone-beam scanner setup A prototype cone beam-CT scanner (Siemens Healthcare, Forchheim, Germany) was used for algorithm validation. A flat-panel detector (PaxScan CB4030, Varian medical systems, Palo Alto, CA) and a modified clinical X-ray tube were mounted on a multislice CT gantry (Bartling et al 2007b, Gupta et al 2006). The flat-panel detector offers 2048 x 1536 elements on an area of 40 x 30 cm², resulting in an element size of 192 µm². The field of view (FOV) is 25 x 25 x 18 cm³. The isotopic resolution, determined by scanning a tungsten wire, is 24 line pairs / cm at 10% modulation transfer function (Gupta et al 2006). The active detector area was limited to 192 x 1024 elements in 2 x 2 pixel binning mode to further increase the readout rate. The used parameters resulted in a FOV of 25 x 25 x 4.5 cm³ and a readout rate of 100 frames per second (fps). This FOV completely covered the chest of a rat in the z direction. Projection data was acquired over 80 s with the gantry rotating continuously 16 times. A tube voltage of 80 kV and a tube current of 50 mA were applied. Slice images were calculated using custom reconstruction software. Reconstructions were performed with a FOV of 22 mm for mice and 45 mm for rats to a slice matrix of 512 x 512 image pixel using a medium hard kernel in a modified FDK algorithm (Feldkamp et al 1984). A slice thickness of 0.2 mm was used, resulting in an image voxel size of 43 x 43 x 200 µm³ (Reconstruction voxel size was selected small enough so that it does not interfere with the scanners resolution). Reconstructed image
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slices were stored as DICOM datasets to assure the usage in conventional medical post-processing software. Multiplanar slice images and three-dimensional (3D) views rendered with commercial software (InSpace Siemens Healthcare, Forchheim, Germany) were used to estimate quality. Semiautomatic segmentations were performed on a CHILI workstation (CHILI GmbH, Heidelberg, Germany) using the Medical Imaging Interaction Toolkit (German Cancer Research Center, Heidelberg, Germany) (Wolf et al 2005). 2.2.6 Motion phantom A motion phantom was used to validate the movement compensation capabilities of the proposed algorithm. In this case, the moving phantom resembled an in vivo situation of a structure affected by physiological motion, while the phantom at rest provided the ground truth. The phantom consisted of an acrylic glass ball that was fixed via a pole to a servo system, enabling an oscillating motion. The diameter of the phantom was measured, and its volume was calculated. The phantom was placed in the scanner system so that a translational movement in z direction at a frequency of 300 min-1 could be initiated, which is comparable with the heart rate of mice. The phantom was scanned five times in motion and five times at rest. The proposed automated gating algorithm was used for a motion-gated reconstruction while a standard reconstruction was used for the datasets of the resting phantom. The resulting images were visually compared for motion artifacts. The volume of the acrylic glass ball was semi-automatically segmented in both data sets to investigate the accuracy of volume quantification. Both the image quality and the segmented volume of the phantom were compared between gated reconstruction of the moving phantom and non-gated reconstruction of the resting phantom. Differences in the volumes that were determined were tested for significance using a t-test with a significance level of 0.05. 2.2.2 Animals To compare the newly proposed fully automated intrinsic gating algorithm with the extrinsic gating algorithm (the gold standard), eight C3H/HeN wild type mice (20 g) and seven healthy Copenhagen rats (250 g) were scanned. The animals were anaesthetized by continuous inhalation of 3% Sevoflurane (Sevorane, Abbot, Maidenhead, UK) in oxygen during preparation, the injection of contrast media and scanning. The pneumatic cushion was placed on the animal bed under the animal’s
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chest to record the respiratory movements. The animals were breathing freely throughout the experiment. To increase the density of the blood and vessels, all animals were scanned after the administration of intravascular iodine containing contrast agent Fenestra-VC (ART Advanced Research Technologies, Saint Laurent, CA). A total amount of 125 mg iodine was injected into the tail vein of the rats 5 min prior to scanning. Mice received 25 mg iodine (Bartling et al 2007b, Badea et al 2005). All experiments were approved by the Governmental Review Committee on Animal Care. 2.2.3 Extrinsic gating hardware A commercially available small animal gating system was employed to derive an extrinsic gating signal for a gold standard comparison. A small animal monitoring unit (1025L and signal breakout module, SA Instruments, Stony Brook, NY, USA) was connected to the scanner's TTL input. ECG electrodes and a pneumatic cushion were attached to the animals. The commercially available software package was used to extract two binary signals that were sent to the gantry once in each cardiac and respiratory cycle. Parameters were chosen so that the gating signal was sent shortly after the R-wave of the ECG and shortly after the start of the inspiration of the animal. 2.2.4 Comparison of image quality The image quality of eight mice was rated by three readers (BLINDED.) for the following structures: (a) the outer heart contours, (b) the inner heart contours, (c) the delineation of central vessels within the mediastinum, and (d) the lung structure in the vicinity of the heart. The following scoring system was used: 0 points: no clear delineation, severe motion artifacts; 1 point: some blurring, contours predominantly assessable; and 2 points: clear delineation, no motion artifacts. Sum scores were calculated for each criterion and each animal. In addition, the mean total sum scores per animal of the non-gated and the intrinsically and extrinsically gated scans in mice were calculated. Differences in the achieved total sum scores were statistically compared using a two-sided Student t-test on depending samples and considering p
