A feasibility study of image registration using ...

Int. J. Biomedical Engineering and Technology, Vol. x, No. x, xxxx

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A feasibility study of image registration using volumetrically classified, motion-free bony landmarks in thoracic 4DCT images for image-guided patient setup Guang Li*, Huchen Xie, Holly Ning, Deborah Citrin, Jacek Capala, Jason Cheng, Barbara C. Arora, C. Norman Coleman, Kevin Camphausen and Robert W. Miller Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bldg. 10, CRC, Rm. B2-3561, 9000 Rockville Pike, Bethesda, Maryland 20892, USA Fax: 301-480-1064 E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] *Corresponding author Abstract: As rigid image registration becomes unreliable in the presence of involuntary organ motion, we present a novel approach to register CT images using stable bony landmarks for image-guided patient setup. Using 3D Volumetric Image Registration (3DVIR) technique, bony anatomy is volumetrically-classified as registration landmark, while soft tissues are ignored. Based on 4DCT, it was found that the spine, posterior ribs and clavicles do not move with respiration and remain registered throughout the breathing cycle. However, mutual information based registration produces an error of 1–2 mm due to moving soft tissues. It is suggested that the 3DVIR can improve image-guided setup. Keywords: 3DVIR; 3D volumetric image registration; 4DCT registration; IGRT; image-guided radiation therapy; image-guided patient setup. Reference to this paper should be made as follows: Li, G., Xie, H., Ning, H., Citrin, D., Capala, J., Cheng, J., Arora, B.C., Coleman, C.N., Camphausen, K. and Miller, R.W. (xxxx) ‘A feasibility study of image registration using volumetrically classified, motion-free bony landmarks in thoracic 4DCT images for image-guided patient setup’, Int. J. Biomedical Engineering and Technology, Vol. x, No. x, pp.xxx–xxx. Copyright © 200x Inderscience Enterprises Ltd.

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G. Li et al. Biographical notes: Guang Li received his BS in Chemical Physics in 1984 and MS in Computational Biophysics in 1987 from the University of Science and Technology of China. He was a faculty member in Southeast University (China) before received his PhD in Analytical Chemistry from Duke University in 1994. He is a Medical Physicist, certified by the American Board of Radiology (ABR) in Therapeutic Radiological Physics, and has been working at the NCI since 2001. His research interests include 3D volumetric image registration, deformable image registration, image motion correction, organ motion prediction and compensation, radiation dosimetry, image-guided radiotherapy, 4D medical imaging and 4D radiotherapy. Huchen Xie received his BS in Electrical Engineering and PhD in Computer Science. He was an Associate Professor in the University of Science and Technology of China before he worked in the Radiation Oncology Branch in the NCI in 1990. His research interests include development of treatment planning system, medical image processing, image segmentation and registration, and Monte Carlo radiation dose computation. Holly Ning received her BS in Electrical Engineering from Xian JiaoTong University, P.R. China in 1982, PhD in Physics from the University of South Carolina in 1991, and MS in Medical Physics from Georgia Institute of Technology in 1993. She was certified by the ABR in therapeutic radiological physics in 1997. She has been working as a Clinical Medical Physicist in the NCI since 1998. Her research interests include new clinical setup, optical fibre dosimetry, image fusion and IGRT. Deborah Citrin received her Undergraduate Degree from North Carolina State University and MD from Duke University School of Medicine in 1999. She completed her Residency at the National Cancer Institute and National Capital Consortium. She joined the NCI as a Staff Physician in 2004 and became a tenure track investigator in 2007. Her research interests include normal tissue radiobiology, malignancies of the gastrointestinal tract, and novel molecular therapeutics combined with radiation. Jacek Capala received his MS in Medical Physics from the Jagiellonian University, Krakow, Poland in 1986 and his PhD in Physical Biology from Uppsala University, Sweden in 1991. He worked in various institutions in the US and Sweden before joined the NCI as a Tenure Track Investigator in 2004. His research interests include Multi-Target approach to targeted therapy aimed cell signalling pathways, and development of Affibody-based bioconjugates for molecular imaging and targeted therapy. Jason Cheng received his BA in Physics and Chemistry from Boston University in 1993, MS in Chemical Engineering from the University of South California in 1995, and PhD in Applied Physics from the University of Maryland in 2002. He is a Postdoctoral fellow and Researching in Monte Carlo radiation dosimetry and image registration. Barbara C. Arora received her BS in Physics and MS in Medical Physics. She is certified by the ABR in Therapeutic and Diagnostic Radiological Physics. She has been working in various institutions and hospitals as a Medical Physicist. She is interested in Collaborative research on clinical treatment planning, image fusion and IGRT. C. Norman Coleman received his MD from Yale University School of Medicine in 1970. He completed his Internship and Residency at the University of California, San Francisco and at the NCI. He was a tenured Faculty Member

Registration of volumetrically-classified bony landmarks in 4DCT

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at the Stanford University School of Medicine before joining Harvard Medical School in 1985. He joined the NCI in 1999 as Director of the Radiation Oncology Sciences Program. His research interests include radiation modification using drugs or biological molecules, and ‘nano-IMRT’ research. Kevin Camphausen received his MD from Georgetown University in 1996. He completed his Internship at Georgetown in 1997 and a residency in radiation oncology at the Joint Center for Radiation Therapy at Harvard Medical School in 2001. He joined the NCI as a Tenure Track Investigator in 2001 and became tenured in 2007. He currently serves as the Chief of the Radiation Oncology Branch. His research interests include imaging and molecular therapeutics through translational research. Robert W. Miller received his BS in Physics 1971 and his MS in Radiological Health in 1974 from the University of Pittsburgh. He received his PhD in Radiological Sciences from the George Washington University in 1998. He joined the NCI in 1981 and is certified by the ABR in Therapeutic Radiological Physics. He has been Acting Chief of the Radiation Physics and Computer Automation Section since 1997. His research interests include radiation dosimetry, optical fibre dosimetry, multi-modality imaging, image fusion, and image-guided radiation therapy.

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Introduction

Patient setup and patient motion are two major sources of uncertainty that affect the target localisation and therefore, the efficacy and toxicity of radiation therapy. These two uncertainties are often intertwined, because many voluntary and involuntary patient motions, such as respiration, are inevitably present during both treatment simulation and treatment delivery. In order to compensate for these uncertainties, a conventional margin, as large as 2 cm over Clinical Target Volume (CTV), is applied to form a Planning Target Volume (PTV), ensuring an adequate radiation delivery (Keall et al., 2006; Hugo et al., 2007). Such an approach, however, allows for excessive exposure of normal tissue contained within the PTV, causing a potential toxicity, which limits the dose that can be delivered. Image-Guided Radiation Therapy (IGRT) has improved the accuracy of patient setup significantly by registering pre-treatment setup images with corresponding planning images and adjusting the patient position accordingly (Xing et al., 2006; Jaffray et al., 2007). In Stereotactic Body Radiation Therapy (SBRT), an overall treatment uncertainty of ±3–5 mm can be achieved (Matsuo et al., 2007; Fuss et al., 2004). Several in-room imaging modalities have been employed for image-guided setup, including 2D X-ray digital radiograph imaging (in kilovoltage, kV, or megavoltage, MV) for beam-eye-view setup alignment (Shirato et al., 2000; Gibbs, 2006), as well as 3D Cone-Beam CT (CBCT) (kV or MV) (Letourneau et al., 2005a; Pouliot et al., 2005) or helical MVCT imaging (Mackie et al., 2003). Image registration plays a key role in improving patient setup in IGRT. Based on the dimension of the pre-treatment images, the image registration can be either 2D-to-2D or 3D-to-3D (Khamene et al., 2006; Court and Dong, 2003; Clippe et al., 2003; Smitsmans et al., 2004; Letourneau et al., 2005b; Forrest et al., 2004; Boswell et al., 2006). In the 2D-to-2D case, one or two orthogonal 2D X-ray radiographs are used

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for registering with Digitally Reconstructed Radiographs (DRR) (Khamene et al., 2006). This registration can be performed using either interactive alignment to bony landmarks or using maximised mutual information automatically, where all of the pixels in the 2D images are employed. In the 3D-to-3D case, automated image registration is preferable, since it is fast, observer independent and has a low failure rate for rigid registration. However, most clinical images contain anatomies that are to some extent deformed, especially for soft tissue. Therefore, automatic registration is susceptible to systematic error and/or can have a higher failure rate. Therefore, it must be validated using a visual-based image fusion method combined with manual adjustment (Forrest et al., 2004; Li et al., 2005). Since all image voxels are utilised in the automatic registration, the result is a compromise between bone structures, which is rigid, and deformable soft tissue. The result is that the accuracy of image-guided patient setup is reduced by the uncertainties caused by patient motion. Patient motion appears in conventional 3D CT images as both soft tissue deformation and motion artefacts. In order to obtain ‘motion-free’ CT images, 4D CT imaging techniques have been established (Vedam et al., 2003; Low et al., 2003; Endo et al., 2003; Li et al., 2006). The 4D approaches can be further categorised as prospective or retrospective, by respiratory gating or sorting, respectively. Both of these approaches are time consuming and have been used in clinical practice. At the image registration level, deformable approaches have been studied for aligning moving soft tissues (Shekhar et al., 2003; Lu et al., 2004; Wang et al., 2005; Brock et al., 2005; Foskey et al., 2005). However, there are two limiting factors: x

a lengthy registration

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difficulty in validation.

In addition, deformable image registration should be employed in target localisation, rather than in patient setup, because it does not provide a set of corrections to a patient’s position. Nevertheless, these two approaches underlie efforts to achieve an integrated IGRT, an important step towards 4D radiation therapy. In this report, we present a new approach for image-guided patient setup, by decoupling moving soft tissue from stable bony landmarks through real-time image classification and visualisation, using 3D Volumetric Image Registration (3DVIR) (Li et al., 2005, 2007). This 3DVIR technique combines registration with image segmentation (classification) and allows an observer to view volumetric bony structures only, by rendering the moving soft tissue transparent in real-time. Therefore, the volumetric registration using bony landmark is unaffected by the presence of the soft tissue motion, providing a more reliable image-guided setup. A set of 4D CT thoracic images was used to x

characterise the mobility of different bony structures under normal respiratory and cardiac motion

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demonstrate the feasibility of 3DVIR registration using bony landmarks alone (without moving soft tissues)

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compare mutual information based image registration with the 3DVIR results.


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Methods and materials

2.1 3D Volumetric Image Registration Technique (3DVIR) This registration technique was developed to simultaneously register up to four image volumes based on intrinsic anatomical landmarks (Li et al., 2005, 2007). The registration criterion was the homogeneity of color distribution on the anatomic landmarks, using a separate mono-colour representation for each image volume. Quantitatively, this criterion was translated as the smallest variance of visible voxel intensity difference of an anatomic landmark. Such registration criteria were used to construct a new dimension beyond the 3D space, achieving both a detection limit and an accuracy of 1/10-voxel, based on three phantom experiments. The volumetric registration software was implemented in C++/Java, supported by both host computer (CPU) as well as a volume rendering board (VolumePro1000, TeraRecon, Inc.), which serves as Graphic Processing Unit (GPU). An array of 32-bit voxel buffers, which were divided into four fields for four voxels (eight-bits each), was used to store up to four images, as shown in Figure 1. Any of the images could be transformed and updated in the visualisation pipeline in real-time. The volume-viewguided image registration was iteratively compared until a satisfactory registration was achieved. Prior to registration, all images were pre-processed automatically to achieve a common Field of View (FOV) with an isotropic voxel size. Figure 1

Flow chart of the 3D volumetric image registration process. The image registration transformation (performed using the CPU) has an interface independent of the image visualisation, which is performed using a Graphical Processing Unit (GPU). Both visual and quantitative registration criteria can be applied to the image registration. Up to four images can be simultaneously registered and their alignment is directly comparable

2.2 Bony landmark classification, visualisation and registration Image visualisation was based on a ray-casting algorithm with RGBA (Red, Green Blue, and Alpha (opacity)) lookup tables controlling the colour and visibility of voxels within an image. The RGBA lookup tables were imbedded inside the visualisation pipeline, so that any interactive changes in the tables via the Graphical User Interface (GUI) were automatically updated in next iteration of the visualisation process.

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With RGBA voxel format, image was classified by altering the Alpha lookup table (voxel opacity filter). A narrow opacity greyscale window was set in the CT image histogram, so that only bony anatomy was visible while other voxels were rendered transparent. A precise window/level setting was not required, due to the large difference in CT greyscale between soft tissues (CT# ~ 0) and bones (CT# ~ 1000). This voxel intensity difference was similar to that of skin-air boundary. A common window/level can be set using one image as a reference, and adjusting the other images to the reference to have the identical or similar visible volume surfaces. Landmark selection and classification was performed interactively at the start of the registration process, but could be performed or changed anytime as needed, such as when a different landmark is selected for cross-verification.

2.3 Patient thoracic 4D CT image acquisition and application A 16-slice CT scanner (Brilliance Big Bore CT, Philips Medical) was employed to produce a 4D CT thoracic image with 10 phases in the respiratory cycle. The bore diameter was 85 cm, while the true FOV was 70 cm. The CT images were acquired at 120 kV and 300 mAs, with a pitch of 0.08. A head and arm holder and knee supporter were employed for patient immobilisation during the 4D CT image acquisition. Patient respiration was monitored using a bellows system, which defined the respiratory phases. The phase-sorted image projections were used to reconstruct the 4D CT images with 10 phases. Maximum inhalation was automatically assigned a name of 0%, and the following nine phases was assigned as 10%, 20%, …, 90%. An averaged CT image, using projections from all phases, was also reconstructed. All of the 4D CT images (512 u 512) were processed automatically, resulting in a 320 u 320 image size with an isotropic voxel size of (1.875 mm)3/voxel and compressed eight-bit grayscale.

2.4 Voxel-based image registration using mutual information The mutual information based image registration software was implemented as described previously (Li et al., 2005). The insight image registration and segmentation toolkits (ITK) were employed for implementing the automatic image registration software. The 0% CT image was used as the fixed reference image and CT images in the remainder of the respiratory phases were used as moving images for rigid image alignment. The original positions of the fixed and moving images were employed as initial positions for optimisation. The registration results were tabulated as physical shifts of the moving images relative to the fixed image (or from their original positions). A statistical analysis was performed on the registration result.

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Results

3.1 Characterisation of the stability of bony structures within the respiratory cycle Figure 2 shows the characterisation of the motion of the bony landmarks. The colour homogeneity of the spine, scapulas, clavicles, and first ribs between the fully inhaled


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phase (0%) and three other exhaled phases (20, 40 and 70%) indicates that these bony structures, under the imaging conditions, do not move with the respiration. These bony structures in the averaged CT image remain stable. Therefore they can be used as reliable landmarks for rigid image registration in the presence of organ motions. Figure 2

The stability of 3D bony anatomies in the respiratory cycle. Three different views are shown in the three rows from the anterior, left-posterior and inferior directions. Column 1 shows the superimposed bony landmarks in phase 0% and phase 20%, column 2 shows those in phase at 0% and 40%, and column 3 shows those in phase at 0% and 70%. The colour of 0% image is red and other phase images are green. A completely superimposed bony landmark appears yellow, synthesised from equally weighted red and green in the two CT images. The arrows point to where the bony motion occurs within the respiratory cycle (see online version for colours)

The remaining ribs, however, are affected by respiration. The rib motion involves mild deformation, since the transformation matrix does not uniformly apply to all ribs. Ribs 2–4 in the CT images can be partially aligned with a shift in the Superior-Inferior (SI) direction by up to 2.5 voxels (