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Kristy K. Brock - One of the best experts on this subject based on the ideXlab platform.
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TU-G-WAB-01: Advanced Applications in Deformable Registration
Medical Physics, 2013Co-Authors: Kristy K. Brock, L Dong, David J. HawkesAbstract:The field of Deformable image Registration has seen many technical advances over the past decade, with increases in accuracy, the speed of the algorithms, and the flexibility of the algorithm to handle complex, multi‐modality images. These technical advances have enabled further advances in the applications of Deformable image Registration. This symposium will cover exciting new applications of Deformable Registration, ranging from population models, to image‐guided treatments outside of external beam radiotherapy, to image validation through correlative pathology. Deformable Registration is generally thought of to be between two images of the same patient, for purposes of aiding the patient's radiotherapy treatment. However, novel applications are using Deformable Registration in applications outside of this space. Examples will include the investigation of population based information, such as the determination of a risk‐based CTV. The use of Deformable Registration for image validation through correlative pathology will be described including challenges and results of early studies. Exciting advances of hybrid Registration technology for robust and efficient Deformable Registration for image‐guided surgery will also be illustrated. Learning Objectives: 1. Describe the use of Deformable Registration for advanced image‐guided treatments. 2. Understand the application of Deformable Registration for population‐based models. 3. Describe the use of Deformable Registration for image validation in correlative pathology. Dr. Brock has financial interest in Deformable Registration technology through a licensing agreement with Ray Search Laboratories.
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TU‐C‐211‐08: Optimization of a Deformable Registration Algorithm for Matching MRI with Histology for Clinical Prostatectomy
Medical Physics, 2011Co-Authors: Navid Samavati, D Mcgrath, Justin Lee, T Vanderkwast, Cynthia Ménard, Kristy K. BrockAbstract:Purpose: Optimize and evaluate a biomechanical model‐based Deformable image Registration algorithm incorporating specimen‐specific changes in material properties for correlating histology of clinical prostatectomy specimens with invivo MRI. Methods: An in‐house biomechanical model‐ based Deformable Registration based on finite element modeling was developed for accurate correlative pathology for clinical prostatectomy. Five image sets were acquired for 4 prostatectomy patients: 1) invivo T2 MRI, 2) exvivo fresh (before fixation) T2 MR, 3) exvivo fixed (after fixation), 4) digital photographs of gross slices after sectioning, and 5) digital whole‐ mount histology images of the slices. A 2D landmark based Deformable Registration technique is used to accurately reconstruct 3D histology volume based on fixed and gross image sets. A 3‐step Deformable Registration based on biomechanics calculates the transformations between histology and fixed, fixed and fresh, and fresh and invivo. Magnetic Resonance Elastography performed on the exvivo tissue provided a specimen specific element‐based Young's modulus map for the fresh and fixed tissue accounting for changes due to fixation. The accuracy of the algorithm was quantified by identifying naturally occurring fiducials in each image and evaluating the Dice overlap index of contoured regions within the prostate. The accuracy was compared to a rigid Registration technique. Results: The average absolute error based on fiducial points in Left‐Right (LR), Anterior‐Posterior (AP), Superior‐ Inferior (SI) directions was 1.3, 1.1, and 1.0 respectively compared to 1.7 (LR), 1.3 (AP), and 1.3 (SI) [mm] achieved with rigid Registration. The maximum absolute error was also reduced from 5.4 with rigid to 3.6 mm with Deformable Registration. A DICE index of 0.67 was achieved using the Deformable Registration, compared to 0.49 with rigid Registration. Conclusions: A biomechanical model‐based Deformable Registration algorithm which incorporates tissue specific changes in material properties has been developed and evaluated on prostatectomy samples, showing marked improvement over rigid Registration.
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Biomechanical model-based Deformable Registration of MRI and histopathology for clinical prostatectomy.
Journal of pathology informatics, 2011Co-Authors: Navid Samavati, Cynthia Ménard, Deirdre M. Mcgrath, Jenny Lee, Theodorus H. Van Der Kwast, Michael A.s. Jewett, Kristy K. BrockAbstract:A biomechanical model-based Deformable image Registration incorporating specimen-specific changes in material properties is optimized and evaluated for correlating histology of clinical prostatectomy specimens with in vivo MRI. In this methodology, a three-step Registration based on biomechanics calculates the transformations between histology and fixed, fixed and fresh, and fresh and in vivo states. A heterogeneous linear elastic material model is constructed based on magnetic resonance elastography (MRE) results. The ex vivo tissue MRE data provide specimen-specific information for the fresh and fixed tissue to account for the changes due to fixation. The accuracy of the algorithm was quantified by calculating the target Registration error (TRE) by identifying naturally occurring anatomical points within the prostate in each image. TRE were improved with the Deformable Registration algorithm compared to rigid Registration alone. The qualitative assessment also showed a good alignment between histology and MRI after the proposed Deformable Registration.
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Results of a multi-institution Deformable Registration accuracy study (MIDRAS).
International Journal of Radiation Oncology - Biology - Physics, 2010Co-Authors: Kristy K. BrockAbstract:PURPOSE: To assess the accuracy, reproducibility, and computational performance of Deformable image Registration algorithms under development at multiple institutions on common datasets. METHODS AND MATERIALS: Datasets from a lung patient (four-dimensional computed tomography [4D-CT]), a liver patient (4D-CT and magnetic resonance imaging [MRI] at exhale), and a prostate patient (repeat MRI) were obtained. Radiation oncologists localized anatomic structures for accuracy assessment. Algorithm accuracy was determined by comparing the computer-predicted displacement at each bifurcation point with the displacement computed from the oncologists' annotations. Thirty-seven academic institutions and medical device manufacturers with published evidence of active Deformable image Registration capabilities were invited to participate. RESULTS: Twenty-seven groups agreed to participate; 6 did not return results. Sixteen completed the liver 4D-CT, 12 the lung 4D-CT, 3 the prostate MRI, and 3 the liver MRI-CT. The range of average absolute error for the lung 4D-CT was 0.6-1.2 mm (left-right [LR]), 0.5-1.8 mm (anterior-posterior [AP]), and 0.7-2.0 mm (superior-inferior [SI]); the liver 4D-CT was 0.8-1.5 mm (LR), 1.0-5.2 mm (AP), and 1.0-5.9 mm (SI); the liver MRI-CT was 1.1-2.6 mm (LR), 2.0-5.0 mm (AP), and 2.2-2.6 mm (SI); and the repeat prostate MRI prostate datasets was 0.5-6.2 mm (LR), 3.1-3.7 mm (AP), and 0.4-2.0 mm (SI). CONCLUSIONS: An infrastructure was developed to assess multi-institution Deformable Registration accuracy. The results indicate large discrepancies in reported shifts, although the majority of Deformable Registration algorithms performed at an accuracy equivalent to the voxel size, promising to improve treatment planning, delivery, and assessment.
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Results of a multi-institution Deformable Registration accuracy study (MIDRAS).
International journal of radiation oncology biology physics, 2009Co-Authors: Kristy K. BrockAbstract:Purpose: To assess the accuracy, reproducibility, and computational performance of Deformable image Registration algorithms under development at multiple institutions on common datasets. Methods and Materials: Datasets from a lung patient (four-dimensional computed tomography [4D-CT]), a liver patient (4D-CTand magnetic resonance imaging [MRI] at exhale), and a prostate patient (repeat MRI) were obtained. Radiation oncologists localized anatomic structures for accuracy assessment. Algorithm accuracy was determined by comparing the computer-predicted displacement at each bifurcation point with the displacement computed from the oncologists’ annotations. Thirty-seven academic institutions and medical device manufacturers with published evidence of active Deformable image Registration capabilities were invited to participate. Results: Twenty-seven groups agreedto participate; 6 didnot return results.Sixteen completed the liver 4D-CT, 12 the lung 4D-CT, 3 the prostate MRI, and 3 the liver MRI-CT. The range of average absolute error for the lung 4DCT was 0.6–1.2 mm (left–right [LR]), 0.5–1.8 mm (anterior–posterior [AP]), and 0.7–2.0 mm (superior–inferior [SI]); the liver 4D-CT was 0.8–1.5 mm (LR), 1.0–5.2 mm (AP), and 1.0–5.9 mm (SI); the liver MRI-CT was 1.1– 2.6 mm (LR), 2.0–5.0 mm (AP), and 2.2–2.6 mm (SI); and the repeat prostate MRI prostate datasets was 0.5–6.2 mm (LR), 3.1–3.7 mm (AP), and 0.4–2.0 mm (SI). Conclusions: An infrastructure was developed to assess multi-institution Deformable Registration accuracy. The results indicate large discrepancies in reported shifts, although the majorityof Deformable Registration algorithms performed at an accuracy equivalent to the voxel size, promising to improve treatment planning, delivery, and assessment. 2010 Elsevier Inc. Deformable Registration, Multi-institution study, Lung, Liver, Prostate.
L Dong - One of the best experts on this subject based on the ideXlab platform.
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TU-G-WAB-01: Advanced Applications in Deformable Registration
Medical Physics, 2013Co-Authors: Kristy K. Brock, L Dong, David J. HawkesAbstract:The field of Deformable image Registration has seen many technical advances over the past decade, with increases in accuracy, the speed of the algorithms, and the flexibility of the algorithm to handle complex, multi‐modality images. These technical advances have enabled further advances in the applications of Deformable image Registration. This symposium will cover exciting new applications of Deformable Registration, ranging from population models, to image‐guided treatments outside of external beam radiotherapy, to image validation through correlative pathology. Deformable Registration is generally thought of to be between two images of the same patient, for purposes of aiding the patient's radiotherapy treatment. However, novel applications are using Deformable Registration in applications outside of this space. Examples will include the investigation of population based information, such as the determination of a risk‐based CTV. The use of Deformable Registration for image validation through correlative pathology will be described including challenges and results of early studies. Exciting advances of hybrid Registration technology for robust and efficient Deformable Registration for image‐guided surgery will also be illustrated. Learning Objectives: 1. Describe the use of Deformable Registration for advanced image‐guided treatments. 2. Understand the application of Deformable Registration for population‐based models. 3. Describe the use of Deformable Registration for image validation in correlative pathology. Dr. Brock has financial interest in Deformable Registration technology through a licensing agreement with Ray Search Laboratories.
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TU‐C‐J‐6B‐09: Automatic Contour Delineation On Subsequent CT Images Using Deformable Registration
Medical Physics, 2005Co-Authors: He Wang, Jennifer O'daniel, Anesa Ahamad, Radhe Mohan, Adam S Garden, L DongAbstract:Purpose: To implement a Deformable Registration algorithm to automatically delineate regions of interest (ROIs) on daily CT images by transforming the corresponding ROIs from a reference CT image. Method and Materials: An intensity-based ‘Demons’ Deformable Registration algorithm was used to find the correspondence between planning CT image and daily CT image. After reference ROIs were delineated manually on the reference CT image, the reference ROIs can be mapped onto each daily CT image. We tested this method on one head-and-neck patient (tonsil tumor). The patient received 3 CT scans per week prior to treatment for a total of 16 CT scans. The deformed ROIs were visually evaluated by a radiation oncologist. The dose-volume histograms (DVHs) of the deformed left and right parotids were calculated and compared with the planned DVHs. In addition, a cumulative dose distribution was calculated by mapping the daily dose distribution from each of the daily CT images back to the planning CT using the Deformable Registration method. Thus, the DVHs from the cumulative dose distribution can be calculated to represent the final “delivered” dose distribution. The volumetric changes during the elapsed treatment days for targets and critical structures were also investigated. Results: The deformed contours reasonably matched with gross anatomy presented by the CT images. The daily DVHs showed a large variation during the course of treatment due to both setup errors and internal organ deformation. In spite of large variation in DVHs of the parotids in daily CT images, the DVHs of the cumulative dose agreed reasonably well with the planned DVHs for both parotids in this case. Conclusion: We demonstrated that an intensity-based Deformable Registration algorithm can be used effectively to map the planning ROIs to subsequent CT images acquired during treatment. This allows effortless replanning in an adaptive CT-guided radiotherapy strategy.
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TU-C-J-6B-09: Automatic Contour Delineation On Subsequent CT Images Using Deformable Registration
Medical Physics, 2005Co-Authors: He Wang, Jennifer O'daniel, Anesa Ahamad, Radhe Mohan, Adam S Garden, L DongAbstract:Purpose: To implement a Deformable Registration algorithm to automatically delineate regions of interest (ROIs) on daily CT images by transforming the corresponding ROIs from a reference CT image. Method and Materials: An intensity-based ‘Demons’ Deformable Registration algorithm was used to find the correspondence between planning CT image and daily CT image. After reference ROIs were delineated manually on the reference CT image, the reference ROIs can be mapped onto each daily CT image. We tested this method on one head-and-neck patient (tonsil tumor). The patient received 3 CT scans per week prior to treatment for a total of 16 CT scans. The deformed ROIs were visually evaluated by a radiation oncologist. The dose-volume histograms (DVHs) of the deformed left and right parotids were calculated and compared with the planned DVHs. In addition, a cumulative dose distribution was calculated by mapping the daily dose distribution from each of the daily CT images back to the planning CT using the Deformable Registration method. Thus, the DVHs from the cumulative dose distribution can be calculated to represent the final “delivered” dose distribution. The volumetric changes during the elapsed treatment days for targets and critical structures were also investigated. Results: The deformed contours reasonably matched with gross anatomy presented by the CT images. The daily DVHs showed a large variation during the course of treatment due to both setup errors and internal organ deformation. In spite of large variation in DVHs of the parotids in daily CT images, the DVHs of the cumulative dose agreed reasonably well with the planned DVHs for both parotids in this case. Conclusion: We demonstrated that an intensity-based Deformable Registration algorithm can be used effectively to map the planning ROIs to subsequent CT images acquired during treatment. This allows effortless replanning in an adaptive CT-guided radiotherapy strategy.
Christos Davatzikos - One of the best experts on this subject based on the ideXlab platform.
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Abnormality Detection via Iterative Deformable Registration and Basis-Pursuit Decomposition
IEEE transactions on medical imaging, 2016Co-Authors: Ke Zeng, Aristeidis Sotiras, Guray Erus, Russell T. Shinohara, Christos DavatzikosAbstract:We present a generic method for automatic detection of abnormal regions in medical images as deviations from a normative data base. The algorithm decomposes an image, or more broadly a function defined on the image grid, into the superposition of a normal part and a residual term. A statistical model is constructed with regional sparse learning to represent normative anatomical variations among a reference population (e.g., healthy controls), in conjunction with a Markov random field regularization that ensures mutual consistency of the regional learning among partially overlapping image blocks. The decomposition is performed in a principled way so that the normal part fits well with the learned normative model, while the residual term absorbs pathological patterns, which may then be detected through a statistical significance test. The decomposition is applied to multiple image features from an individual scan, detecting abnormalities using both intensity and shape information. We form an iterative scheme that interleaves abnormality detection with Deformable Registration, gradually improving robustness of the spatial normalization and precision of the detection. The algorithm is evaluated with simulated images and clinical data of brain lesions, and is shown to achieve robust Deformable Registration and localize pathological regions simultaneously. The algorithm is also applied on images from Alzheimer’s disease patients to demonstrate the generality of the method.
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Deformable Registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy
Magnetic resonance in medicine, 2014Co-Authors: Susan P. Weinstein, Emily F. Conant, Sarah Englander, Bilwaj Gaonkar, Meng-kang Hsieh, Mark A. Rosen, Angela Demichele, Christos DavatzikosAbstract:Purpose To evaluate DRAMMS, an attribute-based Deformable Registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI Registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy. Methods Breast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based Deformable Registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The Registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the Registration deformation. Results DRAMMS had the smallest landmark errors (6.05 ± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 ± 3.86 mm), NMI-FFD (8.21 ± 3.81 mm), SSD-FFD (9.46 ± 4.55 mm), Demons (10.76 ± 6.01 mm), and Diffeomorphic Demons (10.82 ± 6.11 mm). Results show that Registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups. Conclusions The DRAMMS Deformable Registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment. Magn Reson Med 73:2343–2356, 2015. © 2014 Wiley Periodicals, Inc.
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A Comparative Study of Biomechanical Simulators in Deformable Registration of Brain Tumor Images
IEEE Transactions on Biomedical Engineering, 2008Co-Authors: Evangelia I. Zacharaki, Cosmina S. Hogea, George Biros, Christos DavatzikosAbstract:Simulating the brain tissue deformation caused by tumor growth has been found to aid the Deformable Registration of brain tumor images. In this paper, we evaluate the impact that different biomechanical simulators have on the accuracy of Deformable Registration. We use two alternative frameworks for biomechanical simulations of mass effect in 3-D magnetic resonance (MR) brain images. The first one is based on a finite-element model of nonlinear elasticity and unstructured meshes using the commercial software package ABAQUS. The second one employs incremental linear elasticity and regular grids in a fictitious domain method. In practice, biomechanical simulations via the second approach may be at least ten times faster. Landmarks error and visual examination of the coregistered images indicate that the two alternative frameworks for biomechanical simulations lead to comparable results of Deformable Registration. Thus, the computationally less expensive biomechanical simulator offers a practical alternative for Registration purposes.
He Wang - One of the best experts on this subject based on the ideXlab platform.
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TU‐C‐J‐6B‐09: Automatic Contour Delineation On Subsequent CT Images Using Deformable Registration
Medical Physics, 2005Co-Authors: He Wang, Jennifer O'daniel, Anesa Ahamad, Radhe Mohan, Adam S Garden, L DongAbstract:Purpose: To implement a Deformable Registration algorithm to automatically delineate regions of interest (ROIs) on daily CT images by transforming the corresponding ROIs from a reference CT image. Method and Materials: An intensity-based ‘Demons’ Deformable Registration algorithm was used to find the correspondence between planning CT image and daily CT image. After reference ROIs were delineated manually on the reference CT image, the reference ROIs can be mapped onto each daily CT image. We tested this method on one head-and-neck patient (tonsil tumor). The patient received 3 CT scans per week prior to treatment for a total of 16 CT scans. The deformed ROIs were visually evaluated by a radiation oncologist. The dose-volume histograms (DVHs) of the deformed left and right parotids were calculated and compared with the planned DVHs. In addition, a cumulative dose distribution was calculated by mapping the daily dose distribution from each of the daily CT images back to the planning CT using the Deformable Registration method. Thus, the DVHs from the cumulative dose distribution can be calculated to represent the final “delivered” dose distribution. The volumetric changes during the elapsed treatment days for targets and critical structures were also investigated. Results: The deformed contours reasonably matched with gross anatomy presented by the CT images. The daily DVHs showed a large variation during the course of treatment due to both setup errors and internal organ deformation. In spite of large variation in DVHs of the parotids in daily CT images, the DVHs of the cumulative dose agreed reasonably well with the planned DVHs for both parotids in this case. Conclusion: We demonstrated that an intensity-based Deformable Registration algorithm can be used effectively to map the planning ROIs to subsequent CT images acquired during treatment. This allows effortless replanning in an adaptive CT-guided radiotherapy strategy.
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TU-C-J-6B-09: Automatic Contour Delineation On Subsequent CT Images Using Deformable Registration
Medical Physics, 2005Co-Authors: He Wang, Jennifer O'daniel, Anesa Ahamad, Radhe Mohan, Adam S Garden, L DongAbstract:Purpose: To implement a Deformable Registration algorithm to automatically delineate regions of interest (ROIs) on daily CT images by transforming the corresponding ROIs from a reference CT image. Method and Materials: An intensity-based ‘Demons’ Deformable Registration algorithm was used to find the correspondence between planning CT image and daily CT image. After reference ROIs were delineated manually on the reference CT image, the reference ROIs can be mapped onto each daily CT image. We tested this method on one head-and-neck patient (tonsil tumor). The patient received 3 CT scans per week prior to treatment for a total of 16 CT scans. The deformed ROIs were visually evaluated by a radiation oncologist. The dose-volume histograms (DVHs) of the deformed left and right parotids were calculated and compared with the planned DVHs. In addition, a cumulative dose distribution was calculated by mapping the daily dose distribution from each of the daily CT images back to the planning CT using the Deformable Registration method. Thus, the DVHs from the cumulative dose distribution can be calculated to represent the final “delivered” dose distribution. The volumetric changes during the elapsed treatment days for targets and critical structures were also investigated. Results: The deformed contours reasonably matched with gross anatomy presented by the CT images. The daily DVHs showed a large variation during the course of treatment due to both setup errors and internal organ deformation. In spite of large variation in DVHs of the parotids in daily CT images, the DVHs of the cumulative dose agreed reasonably well with the planned DVHs for both parotids in this case. Conclusion: We demonstrated that an intensity-based Deformable Registration algorithm can be used effectively to map the planning ROIs to subsequent CT images acquired during treatment. This allows effortless replanning in an adaptive CT-guided radiotherapy strategy.
Gustavo H. Olivera - One of the best experts on this subject based on the ideXlab platform.
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Deformable Registration of the planning image (kVCT) and the daily images (MVCT) for adaptive radiation therapy
Physics in medicine and biology, 2006Co-Authors: Gustavo H. Olivera, Quan Chen, Kenneth J. Ruchala, Jason Haimerl, Sanford L. Meeks, Katja M. Langen, Patrick A. KupelianAbstract:The incorporation of daily images into the radiotherapy process leads to adaptive radiation therapy (ART), in which the treatment is evaluated periodically and the plan is adaptively modified for the remaining course of radiotherapy. Deformable Registration between the planning image and the daily images is a key component of ART. In this paper, we report our researches on Deformable Registration between the planning kVCT and the daily MVCT image sets. The method is based on a fast intensity-based free-form Deformable Registration technique. Considering the noise and contrast resolution differences between the kVCT and the MVCT, an 'edge-preserving smoothing' is applied to the MVCT image prior to the Deformable Registration process. We retrospectively studied daily MVCT images from commercial TomoTherapy machines from different clinical centers. The data set includes five head-neck cases, one pelvis case, two lung cases and one prostate case. Each case has one kVCT image and 20-40 MVCT images. We registered the MVCT images with their corresponding kVCT image. The similarity measures and visual inspections of contour matches by physicians validated this technique. The applications of Deformable Registration in ART, including 'Deformable dose accumulation', 'automatic re-contouring' and 'tumour growth/regression evaluation' throughout the course of radiotherapy are also studied.
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WE‐D‐I‐6B‐08; Deformable Registration of the Planning Image (KVCT) and Daily Treatment Images (MVCT) for Adaptive Radiation Therapy
Medical Physics, 2005Co-Authors: Quan Chen, Kenneth J. Ruchala, Sanford L. Meeks, Katja M. Langen, Patrick A. Kupelian, Gustavo H. OliveraAbstract:Purpose: The incorporation of daily images into the radiotherapy process leads to Adaptive Radiation Therapy(ART), in which the treatment is evaluated periodically and the plan is adaptively modified during the whole course of radiotherapy. Deformable Registration between the planning image (usually KVCT) and the daily image is the key component of ART. The MVCT is one of the most informative yet convenient daily image modalities. In this presentation, we developed a fast technique for Deformable Registration between the KVCT image and the MVCT images.Method and Materials: The method is extended from the state‐of‐art Deformable Registration technique, which is fast and accurate for same modality Registration. Considering the higher noise and lower contrast nature of MVCTs, special techniques such as “edge‐preserving smoothing” and “reference based histogram calibration” are applied before the Deformable Registration process. The whole process is around 2–3 minutes for typical MVCT size (256×256×64) when runs on a single processor PC. We retrospectively studied daily MVCTs from commercial TomoTherapy machines in different clinical centers. These data include 3 lung cases, 5 head‐neck cases, 3 prostate cases and 2 pelvis cases. Each case has one KVCT image and 30–40 MVCT images. We registered the MVCT images with their corresponding KVCT image.Results: The similarity measures and visual inspection of contour matches by physicians validate this technique. The applications of Deformable Registration in ART, including “accumulative dose calculation”, “automatic ROI re‐contouring” and “tumor growth/shrinkage monitoring” through the whole course of radiotherapy are studied. Conclusion: Deformable Registration between the KVCT and MVCT images is an essential step towards ART. Through the combination of conventional image processing techniques and the fast intensity based Deformable Registration, such task becomes feasible. Extensive tests based on the daily MVCT data from the TomoTherapy machines validate such technique. Several key components of ART are developed.