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Kristy K. Brock - One of the best experts on this subject based on the ideXlab platform.

  • Deformable Image Registration of heterogeneous human lung incorporating the bronchial tree.
    Medical physics, 2010
    Co-Authors: Adil Al-mayah, Joanne Moseley, M. Velec, Shannon Hunter, Kristy K. Brock
    Abstract:

    Purpose: To investigate the effect of the bronchial tree on the accuracy of biomechanical-based Deformable Image Registration of human lungs. Methods: Three dimensional finite element models have been developed using four dimensional computed tomography Image data of ten lung cancer patients. Each model is built of a body, left and right lungs, tumor, and bronchial trees. Triangular shell elements are used for the bronchial trees while tetrahedral elements are used for other components. Hyperelastic material properties based on experimental investigation on human lungs are used for the lung parenchyma. Different material properties are assigned for the bronchial tree using five values for the modulus of elasticity of 0.01, 0.12, 0.5, 10, and 18 MPa. Lungs are modeled to slide inside chest cavities by applying frictionless contact surfaces between each lung and corresponding chest cavity. The accuracy of the models is examined using an average of 40 bronchial bifurcation points identified on inhale and exhale Images. Relative accuracy is evaluated by comparing the displacement of all nodes within the lungs as well as the dosimetric difference at the exhale position predicted by the model. Results: There is no significant effect of bronchial tree on the model accuracy based on the bifurcation points analysis. However, on the local level, using an average of 38 000 nodes, there is a maximum difference of 8.5 mm in the deformation of the bronchial trees, as the modulus of elasticity of the bronchial trees increases from 0.01 to 18 MPa; however, more than 96% of nodes are within a 2.5 mm difference in each direction. The average dose difference at the predicted exhale position is less than 35 cGy between the models. Conclusions: The bronchial tree has little effect on the global deformation and the accuracy of Deformable Image Registration of lungs. Hence, the homogenous model is a reasonable assumption. Since there are some local deformation differences between nodes as the material properties of the bronchial tree change that may affect the accuracy of dosimetric results, heterogeneity may be required for a smaller scale modeling of lungs.

  • Assessment of a Model-Based Deformable Image Registration Approach for Radiation Therapy Planning
    International journal of radiation oncology biology physics, 2007
    Co-Authors: Michael Kaus, Vladimir Pekar, Kristy K. Brock, Laura A. Dawson, Alan Nichol, David A. Jaffray
    Abstract:

    Purpose: The aim of this study is to develop a surface-based Deformable Image Registration strategy and to assess the accuracy of the system for the integration of multimodality imaging, Image-guided radiation therapy, and assessment of geometrical change during and after therapy. Methods and Materials: A surface-model–based Deformable Image Registration system has been developed that enables quantitative description of geometrical change in multimodal Images with high computational efficiency. Based on the deformation of organ surfaces, a volumetric deformation field is derived using different volumetric elasticity models as alternatives to finite-element modeling. Results: The accuracy of the system was assessed both visually and quantitatively by tracking naturally occurring landmarks (bronchial bifurcations in the lung, vessel bifurcations in the liver, implanted gold markers in the prostate). The maximum displacements for lung, liver and prostate were 5.3 cm, 3.2 cm, and 0.6 cm respectively. The largest Registration error (direction, mean ± SD) for lung, liver and prostate were (inferior–superior, −0.21 ± 0.38 cm), (anterior–posterior, −0.09 ± 0.34 cm), and (left–right, 0.04 ± 0.38 cm) respectively, which was within the Image resolution regardless of the deformation model. The computation time (2.7 GHz Intel Xeon) was on the order of seconds ( e.g. , 10 s for 2 prostate datasets), and deformed axial Images could be viewed at interactive speed (less than 1 s for 512 × 512 voxels). Conclusions: Surface-based Deformable Image Registration enables the quantification of geometrical change in normal tissue and tumor with acceptable accuracy and speed.

  • TU‐C‐ValA‐05: Assessment of a Model‐Based Deformable Image Registration Approach for Radiotherapy Planning
    Medical Physics, 2006
    Co-Authors: Michael Kaus, Vladimir Pekar, Kristy K. Brock, Laura A. Dawson, Alan Nichol, Karl Antonin Bzdusek, D. Jaffray
    Abstract:

    Purpose: To assess the accuracy of a surface‐based Deformable Image Registration strategy as a function of the elasticitymodel for the integration of multi‐modality imaging,Image‐guidedradiation therapy, and quantification of geometrical change during and following therapy. Method and Materials: A surface‐model based Deformable Image Registration system has been developed that enables quantitative description of geometrical change in multi‐modal Images. Based on the deformation of organ surfaces represented by triangular surface meshes, a volumetric deformation field is derived using different volumetric elasticitymodels (Thin‐Plate Splines, Wendland functions, Elastic Body Splines) as alternatives to finite‐element modeling.Results: The system was demonstrated on five livercancer patients, ten prostate cancer patients, thorax in five healthy volunteers, and abdomen in five healthy volunteers. The accuracy of the system was assessed by tracking visible fiducials (bronchial bifurcations in the lung, vessel bifurcations in the liver, implanted gold markers in the prostate). The maximum displacements for lung,liver and prostate were 5.3 cm, 3.2 cm, and 1.8 cm respectively. The largest Registration error (direction, mean ± standard deviation) for lung,liver and prostate were (inferior‐superior, −0.21 ± 0.38 cm), (anterior‐posterior, −0.09 ± 0.34 cm), and (left‐right, 0.04 ± 0.38 cm) respectively, which was within the Image resolution regardless of the deformation model. The computation time (2.7 GHz Intel Xeon) was on the order of seconds (e.g. 10 seconds for two prostate data sets), and Image deformation results could be viewed at interactive speed (less than 1 second for 512×512 voxels). Conclusion: Surface‐based Deformable Image Registration enables the quantification of geometrical change in normal tissue and tumor with acceptable accuracy and speed.

  • FEASIBILITY OF A NOVEL Deformable Image Registration TECHNIQUE TO FACILITATE CLASSIFICATION, TARGETING, AND MONITORING OF TUMOR AND NORMAL TISSUE
    International journal of radiation oncology biology physics, 2006
    Co-Authors: Kristy K. Brock, Laura A. Dawson, Michael B. Sharpe, Douglas J. Moseley, David A. Jaffray
    Abstract:

    Purpose: To investigate the feasibility of a biomechanical-based Deformable Image Registration technique for the integration of multimodality imaging, Image guided treatment, and response monitoring. Methods and Materials: A multiorgan Deformable Image Registration technique based on finite element modeling (FEM) and surface projection alignment of selected regions of interest with biomechanical material and interface models has been developed. FEM also provides an inherent method for direct tracking specified regions through treatment and follow-up. Results: The technique was demonstrated on 5 liver cancer patients. Differences of up to 1 cm of motion were seen between the diaphragm and the tumor center of mass after Deformable Image Registration of exhale and inhale CT scans. Spatial differences of 5 mm or more were observed for up to 86% of the surface of the defined tumor after Deformable Image Registration of the computed tomography (CT) and magnetic resonance Images. Up to 6.8 mm of motion was observed for the tumor after Deformable Image Registration of the CT and cone-beam CT scan after rigid Registration of the liver. Deformable Registration of the CT to the follow-up CT allowed a more accurate assessment of tumor response. Conclusions: This biomechanical-based Deformable Image Registration technique incorporates classification, targeting, and monitoring of tumor and normal tissue using one methodology.

  • accuracy of finite element model based multi organ Deformable Image Registration
    Medical Physics, 2005
    Co-Authors: Kristy K. Brock, Laura A. Dawson, Michael B. Sharpe, Sun Mo Kim, David A. Jaffray
    Abstract:

    As more pretreatment imaging becomes integrated into the treatment planning process and full three-dimensional Image-guidance becomes part of the treatment delivery the need for a Deformable Image Registration technique becomes more apparent. A novel finite element model-based multi-organ Deformable Image Registration method, MORFEUS, has been developed. The basis of this method is twofold: first, individual organ deformation can be accurately modeled by deforming the surface of the organ at one instance into the surface of the organ at another instance and assigning the material properties that allow the internal structures to be accurately deformed into the secondary position and second, multi-organ Deformable alignment can be achieved by explicitly defining the deformation of a subset of organs and assigning surface interfaces between organs. The feasibility and accuracy of the method was tested on MR thoracic and abdominal Images of healthy volunteers at inhale and exhale. For the thoracic cases, the lungs and external surface were explicitly deformed and the breasts were implicitly deformed based on its relation to the lung and external surface. For the abdominal cases, the liver, spleen, and external surface were explicitly deformed and the stomach and kidneys were implicitly deformed. The average accuracy (average absolute error) of the lung and liver deformation, determined by tracking visible bifurcations, was 0.19 (s.d.: 0.09), 0.28 (s.d.: 0.12) and 0.17 ( s.d. : 0.07 ) cm , in the LR, AP, and IS directions, respectively. The average accuracy of implicitly deformed organs was 0.11 (s.d.: 0.11), 0.13 (s.d.: 0.12), and 0.08 ( s.d. : 0.09 ) cm , in the LR, AP, and IS directions, respectively. The average vector magnitude of the accuracy was 0.44 ( s.d. : 0.20 ) cm for the lung and liver deformation and 0.24 ( s.d. : 0.18 ) cm for the implicitly deformed organs. The two main processes, explicit deformation of the selected organs and finite element analysis calculations, require less than 120 and 495 s , respectively. This platform can facilitate the integration of Deformable Image Registration into online Image guidance procedures, dose calculations, and tissue response monitoring as well as performing multi-modality Image Registration for purposes of treatment planning.

Radhe Mohan - One of the best experts on this subject based on the ideXlab platform.

  • Improving soft-tissue contrast in four-dimensional computed tomography Images of liver cancer patients using a Deformable Image Registration method.
    International journal of radiation oncology biology physics, 2008
    Co-Authors: He Wang, Radhe Mohan, Sunil Krishnan, Xiaochun Wang, A. Sam Beddar, Tina Marie Briere, Christopher H. Crane, Lei Dong
    Abstract:

    Purpose To investigate a Deformable Image Registration method to improve soft-tissue contrast in four-dimensional (4D) computed tomography (CT) Images of the liver. Methods and Materials Ten patients with hepatocellular carcinoma underwent 4D CT scan for radiotherapy treatment planning on a positron emission tomography/CT scanner. Four-dimensional CT Images were binned into 10 equispaced phases. The exhale phase served as the reference phase, and Images from the other nine phases were coregistered to the reference phase Image using an intensity-based, automatic Deformable Image Registration method. Then the coregistered Images were combined to create a single, high-quality reconstructed CT Image at exhale phase as the new reference for target delineation. The extent of Image quality enhancement was quantified relative to the original CT by calculating the signal-to-noise ratio and the contrast-to-noise ratio. Results The soft tissue Image contrast was noticeably better after Deformable Image Registration than in the original scans. Signal-to-noise ratios inside the liver region of interest increased for all patients by a factor of 3.0 (range, 2.3–3.7). The improvement in Image quality was not linearly proportionate to the number of Images averaged. Using only 6 phases can achieve at least 85% of the contrast enhancement that can be achieved using all 10 phases. We also found that contrast enhancement was inversely proportional to the original Image quality ( p = 0.006), and the contrast enhancement is attained with little loss of spatial resolution. Conclusions This Deformable Image Registration method is feasible to improve soft-tissue Image quality in 4D CT Images.

  • SU‐GG‐I‐101: A Parallel Implementation for Fast Deformable Image Registration of Large Data Sets
    Medical Physics, 2008
    Co-Authors: Liang Zhang, Radhe Mohan, Yuqing Zhang, L. Dong
    Abstract:

    Purpose: One major obstacle for using Deformable Image Registration for online or near real‐time applications is its high computational cost. The goal of this study is to implement a practical parallel computing technique for Image intensity‐based Deformable Registration of relatively large data sets.Method and Materials: Taking advantage of the locality characteristic of the deformed Images, Deformable Image Registration can be parallelized by dividing the entire volume into smaller sub‐volumes. We implemented a parallel solution with the single‐program‐multiple‐data (SPMD) programming model in a cluster. The cluster is composed of one lead computer and 12 nodes. To ensure continuity at boundaries of sub‐volumes, we added an overlapping volume, which became a computational overhead. We proposed two techniques to reduce the overlapping volume by applying pre‐Registration during the coarse levels of a multi‐resolution approach, and by handling the boundary condition carefully when applying the smoothing filter during Registration. The baseline data for comparison was the deformation result from one CPU. Results were compared in three difficult cases with large deformation and large data set. The scalability was also analyzed. Results: 8‐1 nodes appears to be enough to handle each case. Four overlapping slices combined with pre‐Registration at coarse level were able to get the correct deformation field at the boundary of each sub‐volumes even in the presence of relatively large deformations. Computing times were 20.5s, 44.4s, and 76.5s for a prostate case (matrix size: 294*237*62), a head & neck case (361*414*87), and a breast case (465*371*157), respectively. The same data sets required 54.5s, 150.2s, and 331.6s to compute on a single CPU, respectively. Conclusion: We have implemented an effective parallel solution for Deformable Image Registration which can handle large data sets in a short time.

  • A Deformable Image Registration method to handle distended rectums in prostate cancer radiotherapy.
    Medical physics, 2006
    Co-Authors: Song Gao, Renaud De Crevoisier, Lifei Zhang, He Wang, D. Kuban, Radhe Mohan, Lei Dong
    Abstract:

    In Image-guided adaptive radiotherapy, it is important to have the capability to automatically and accurately delineate the rectal wall, which is a major dose-limiting organ in prostate cancer radiotherapy. As Image Registration is a process to find the spatial correspondence between two Images, a major challenge in intensity-based Deformable Image Registration is to deal with the situation where no correspondence exists for some objects between the two Images to be registered. One example is the variation of rectal contents due to the presence and absence of bowel gas. The intensity-based Deformable Image Registration methods alone cannot create the correct spatial transformation if there is no correspondence between the source and target Images. In this study we implemented an automatic Image intensity modification procedure to create artificial gas pockets in the planning computed tomography (CT) Images. A diffusion-based Deformable Image Registration algorithm was developed to use an adaptive smoothing algorithm to better handle large organ deformations. The process was tested in 15 prostate cancer cases and 30 daily CT Images containing the largest distended rectums. The manually delineated rectums agreed well with the autodelineated rectums when using the Image-intensity modification procedure.

  • SU‐EE‐A3‐06: Deformable Image Registration in Cone‐Beam CT Images for Image‐Guided Adaptive Radiotherapy
    Medical Physics, 2006
    Co-Authors: Lifei Zhang, L. Dong, Xiaorong Ronald Zhu, Adam S. Garden, Anesa Ahamad, Kian K. Ang, Radhe Mohan
    Abstract:

    Purpose: With the availability of on‐board imaging devices capable of constructing cone‐beam CT(CBCT)Images, it is expected that there will be great interest in using volumetric CBCT for Image‐guided adaptive radiotherapy. In order to fully utilize CBCT, automatic segmentation on CBCTImages is one of key steps toward this goal. The purpose of this study is to implement a robust Deformable Image Registration for auto‐segmentation. Method and Materials: In four head and neck cancer patients, we used our previously developed, Image intensity‐based Deformable Image Registration algorithm to register the planning CT with the 3–5 daily CBCT in three scenarios. First, the daily CBCT was directly used without modification. Second, we applied a generic look‐up‐table transformation to map the CBCTImage intensity to the conventional CT intensity using the measured electron density calibration curves for both the conventional and CBCTscanners. In the third scenario, we proposed a wavelet‐based dynamic window/level histogram matching algorithm to map the CT number from CBCTImage to the conventional CTImage. Then the Deformable Image Registration was performed in the modified CBCTImages to map the anatomical structures from the planning CT to the corresponding CBCTImages.Results: Without pre‐processing, we found that the CT numbers in CBCTImages were inconsistent, especially in soft tissue regions and in patients with large body circumferences. The Deformable Image Registration using the window/level histogram matching method performed the best with good consistency in delineating soft tissue structures. The algorithm is also computationally efficient. Conclusion: We implemented a wavelet‐based window/level histogram matching algorithm to pre‐process the CBCT to allow for more robust Deformable Image Registration of with the reference planning CT. This implementation allows for volumetric CBCT‐guided adaptive radiotherapy.

  • Validation of an accelerated 'demons' algorithm for Deformable Image Registration in radiation therapy.
    Physics in medicine and biology, 2005
    Co-Authors: He Wang, Radhe Mohan, Lei Dong, Adam S. Garden, Jennifer O'daniel, K. Kian Ang, Deborah A. Kuban, M. Bonnen, Joe Y. Chang, Rex Cheung
    Abstract:

    A greyscale-based fully automatic Deformable Image Registration algorithm, originally known as the 'demons' algorithm, was implemented for CT Image-guided radiotherapy. We accelerated the algorithm by introducing an 'active force' along with an adaptive force strength adjustment during the iterative process. These improvements led to a 40% speed improvement over the original algorithm and a high tolerance of large organ deformations. We used three methods to evaluate the accuracy of the algorithm. First, we created a set of mathematical transformations for a series of patient's CT Images. This provides a 'ground truth' solution for quantitatively validating the Deformable Image Registration algorithm. Second, we used a physically Deformable pelvic phantom, which can measure deformed objects under different conditions. The results of these two tests allowed us to quantify the accuracy of the Deformable Registration. Validation results showed that more than 96% of the voxels were within 2 mm of their intended shifts for a prostate and a head-and-neck patient case. The mean errors and standard deviations were 0.5 mm+/-1.5 mm and 0.2 mm+/-0.6 mm, respectively. Using the Deformable pelvis phantom, the result showed a tracking accuracy of better than 1.5 mm for 23 seeds implanted in a phantom prostate that was deformed by inflation of a rectal balloon. Third, physician-drawn contours outlining the tumour volumes and certain anatomical structures in the original CT Images were deformed along with the CT Images acquired during subsequent treatments or during a different respiratory phase for a lung cancer case. Visual inspection of the positions and shapes of these deformed contours agreed well with human judgment. Together, these results suggest that the accelerated demons algorithm has significant potential for delineating and tracking doses in targets and critical structures during CT-guided radiotherapy.

David A. Jaffray - One of the best experts on this subject based on the ideXlab platform.

  • Assessment of a Model-Based Deformable Image Registration Approach for Radiation Therapy Planning
    International journal of radiation oncology biology physics, 2007
    Co-Authors: Michael Kaus, Vladimir Pekar, Kristy K. Brock, Laura A. Dawson, Alan Nichol, David A. Jaffray
    Abstract:

    Purpose: The aim of this study is to develop a surface-based Deformable Image Registration strategy and to assess the accuracy of the system for the integration of multimodality imaging, Image-guided radiation therapy, and assessment of geometrical change during and after therapy. Methods and Materials: A surface-model–based Deformable Image Registration system has been developed that enables quantitative description of geometrical change in multimodal Images with high computational efficiency. Based on the deformation of organ surfaces, a volumetric deformation field is derived using different volumetric elasticity models as alternatives to finite-element modeling. Results: The accuracy of the system was assessed both visually and quantitatively by tracking naturally occurring landmarks (bronchial bifurcations in the lung, vessel bifurcations in the liver, implanted gold markers in the prostate). The maximum displacements for lung, liver and prostate were 5.3 cm, 3.2 cm, and 0.6 cm respectively. The largest Registration error (direction, mean ± SD) for lung, liver and prostate were (inferior–superior, −0.21 ± 0.38 cm), (anterior–posterior, −0.09 ± 0.34 cm), and (left–right, 0.04 ± 0.38 cm) respectively, which was within the Image resolution regardless of the deformation model. The computation time (2.7 GHz Intel Xeon) was on the order of seconds ( e.g. , 10 s for 2 prostate datasets), and deformed axial Images could be viewed at interactive speed (less than 1 s for 512 × 512 voxels). Conclusions: Surface-based Deformable Image Registration enables the quantification of geometrical change in normal tissue and tumor with acceptable accuracy and speed.

  • FEASIBILITY OF A NOVEL Deformable Image Registration TECHNIQUE TO FACILITATE CLASSIFICATION, TARGETING, AND MONITORING OF TUMOR AND NORMAL TISSUE
    International journal of radiation oncology biology physics, 2006
    Co-Authors: Kristy K. Brock, Laura A. Dawson, Michael B. Sharpe, Douglas J. Moseley, David A. Jaffray
    Abstract:

    Purpose: To investigate the feasibility of a biomechanical-based Deformable Image Registration technique for the integration of multimodality imaging, Image guided treatment, and response monitoring. Methods and Materials: A multiorgan Deformable Image Registration technique based on finite element modeling (FEM) and surface projection alignment of selected regions of interest with biomechanical material and interface models has been developed. FEM also provides an inherent method for direct tracking specified regions through treatment and follow-up. Results: The technique was demonstrated on 5 liver cancer patients. Differences of up to 1 cm of motion were seen between the diaphragm and the tumor center of mass after Deformable Image Registration of exhale and inhale CT scans. Spatial differences of 5 mm or more were observed for up to 86% of the surface of the defined tumor after Deformable Image Registration of the computed tomography (CT) and magnetic resonance Images. Up to 6.8 mm of motion was observed for the tumor after Deformable Image Registration of the CT and cone-beam CT scan after rigid Registration of the liver. Deformable Registration of the CT to the follow-up CT allowed a more accurate assessment of tumor response. Conclusions: This biomechanical-based Deformable Image Registration technique incorporates classification, targeting, and monitoring of tumor and normal tissue using one methodology.

  • accuracy of finite element model based multi organ Deformable Image Registration
    Medical Physics, 2005
    Co-Authors: Kristy K. Brock, Laura A. Dawson, Michael B. Sharpe, Sun Mo Kim, David A. Jaffray
    Abstract:

    As more pretreatment imaging becomes integrated into the treatment planning process and full three-dimensional Image-guidance becomes part of the treatment delivery the need for a Deformable Image Registration technique becomes more apparent. A novel finite element model-based multi-organ Deformable Image Registration method, MORFEUS, has been developed. The basis of this method is twofold: first, individual organ deformation can be accurately modeled by deforming the surface of the organ at one instance into the surface of the organ at another instance and assigning the material properties that allow the internal structures to be accurately deformed into the secondary position and second, multi-organ Deformable alignment can be achieved by explicitly defining the deformation of a subset of organs and assigning surface interfaces between organs. The feasibility and accuracy of the method was tested on MR thoracic and abdominal Images of healthy volunteers at inhale and exhale. For the thoracic cases, the lungs and external surface were explicitly deformed and the breasts were implicitly deformed based on its relation to the lung and external surface. For the abdominal cases, the liver, spleen, and external surface were explicitly deformed and the stomach and kidneys were implicitly deformed. The average accuracy (average absolute error) of the lung and liver deformation, determined by tracking visible bifurcations, was 0.19 (s.d.: 0.09), 0.28 (s.d.: 0.12) and 0.17 ( s.d. : 0.07 ) cm , in the LR, AP, and IS directions, respectively. The average accuracy of implicitly deformed organs was 0.11 (s.d.: 0.11), 0.13 (s.d.: 0.12), and 0.08 ( s.d. : 0.09 ) cm , in the LR, AP, and IS directions, respectively. The average vector magnitude of the accuracy was 0.44 ( s.d. : 0.20 ) cm for the lung and liver deformation and 0.24 ( s.d. : 0.18 ) cm for the implicitly deformed organs. The two main processes, explicit deformation of the selected organs and finite element analysis calculations, require less than 120 and 495 s , respectively. This platform can facilitate the integration of Deformable Image Registration into online Image guidance procedures, dose calculations, and tissue response monitoring as well as performing multi-modality Image Registration for purposes of treatment planning.

  • Accuracy of finite element model‐based multi‐organ Deformable Image Registration
    Medical physics, 2005
    Co-Authors: Kristy K. Brock, Laura A. Dawson, Michael B. Sharpe, Sun Mo Kim, David A. Jaffray
    Abstract:

    As more pretreatment imaging becomes integrated into the treatment planning process and full three-dimensional Image-guidance becomes part of the treatment delivery the need for a Deformable Image Registration technique becomes more apparent. A novel finite element model-based multi-organ Deformable Image Registration method, MORFEUS, has been developed. The basis of this method is twofold: first, individual organ deformation can be accurately modeled by deforming the surface of the organ at one instance into the surface of the organ at another instance and assigning the material properties that allow the internal structures to be accurately deformed into the secondary position and second, multi-organ Deformable alignment can be achieved by explicitly defining the deformation of a subset of organs and assigning surface interfaces between organs. The feasibility and accuracy of the method was tested on MR thoracic and abdominal Images of healthy volunteers at inhale and exhale. For the thoracic cases, the lungs and external surface were explicitly deformed and the breasts were implicitly deformed based on its relation to the lung and external surface. For the abdominal cases, the liver, spleen, and external surface were explicitly deformed and the stomach and kidneys were implicitly deformed. The average accuracy (average absolute error) of the lung and liver deformation, determined by tracking visible bifurcations, was 0.19 (s.d.: 0.09), 0.28 (s.d.: 0.12) and 0.17 ( s.d. : 0.07 ) cm , in the LR, AP, and IS directions, respectively. The average accuracy of implicitly deformed organs was 0.11 (s.d.: 0.11), 0.13 (s.d.: 0.12), and 0.08 ( s.d. : 0.09 ) cm , in the LR, AP, and IS directions, respectively. The average vector magnitude of the accuracy was 0.44 ( s.d. : 0.20 ) cm for the lung and liver deformation and 0.24 ( s.d. : 0.18 ) cm for the implicitly deformed organs. The two main processes, explicit deformation of the selected organs and finite element analysis calculations, require less than 120 and 495 s , respectively. This platform can facilitate the integration of Deformable Image Registration into online Image guidance procedures, dose calculations, and tissue response monitoring as well as performing multi-modality Image Registration for purposes of treatment planning.

Thomas Guerrero - One of the best experts on this subject based on the ideXlab platform.

  • Deformable Image Registration for temporal subtraction of chest radiographs
    Computer Assisted Radiology and Surgery, 2014
    Co-Authors: Edward M Castillo, Hong Yan Luo, Xiao Lin Zheng, Richard Castillo, D Meshkov, Thomas Guerrero
    Abstract:

    Purpose Temporal subtraction Images constructed from Image Registration can facilitate the visualization of pathologic changes. In this study, we propose a Deformable Image Registration (DIR) framework for creating temporal subtraction Images of chest radiographs.

  • four dimensional Deformable Image Registration using trajectory modeling
    Physics in Medicine and Biology, 2010
    Co-Authors: Edward M Castillo, Richard Castillo, Josue G Martinez, Maithili Shenoy, Thomas Guerrero
    Abstract:

    A four-dimensional Deformable Image Registration (4D DIR) algorithm, referred to as 4D local trajectory modeling (4DLTM), is presented and applied to thoracic 4D computed tomography (4DCT) Image sets. The theoretical framework on which this algorithm is built exploits the incremental continuity present in 4DCT component Images to calculate a dense set of parameterized voxel trajectories through space as functions of time. The spatial accuracy of the 4DLTM algorithm is compared with an alternative Registration approach in which component phase to phase (CPP) DIR is utilized to determine the full displacement between maximum inhale and exhale Images. A publically available DIR reference database (http://www.dir-lab.com) is utilized for the spatial accuracy assessment. The database consists of ten 4DCT Image sets and corresponding manually identified landmark points between the maximum phases. A subset of points are propagated through the expiratory 4DCT component Images. Cubic polynomials were found to provide sufficient flexibility and spatial accuracy for describing the point trajectories through the expiratory phases. The resulting average spatial error between the maximum phases was 1.25 mm for the 4DLTM and 1.44 mm for the CPP. The 4DLTM method captures the long-range motion between 4DCT extremes with high spatial accuracy.

D. Jaffray - One of the best experts on this subject based on the ideXlab platform.

  • TU‐C‐ValA‐05: Assessment of a Model‐Based Deformable Image Registration Approach for Radiotherapy Planning
    Medical Physics, 2006
    Co-Authors: Michael Kaus, Vladimir Pekar, Kristy K. Brock, Laura A. Dawson, Alan Nichol, Karl Antonin Bzdusek, D. Jaffray
    Abstract:

    Purpose: To assess the accuracy of a surface‐based Deformable Image Registration strategy as a function of the elasticitymodel for the integration of multi‐modality imaging,Image‐guidedradiation therapy, and quantification of geometrical change during and following therapy. Method and Materials: A surface‐model based Deformable Image Registration system has been developed that enables quantitative description of geometrical change in multi‐modal Images. Based on the deformation of organ surfaces represented by triangular surface meshes, a volumetric deformation field is derived using different volumetric elasticitymodels (Thin‐Plate Splines, Wendland functions, Elastic Body Splines) as alternatives to finite‐element modeling.Results: The system was demonstrated on five livercancer patients, ten prostate cancer patients, thorax in five healthy volunteers, and abdomen in five healthy volunteers. The accuracy of the system was assessed by tracking visible fiducials (bronchial bifurcations in the lung, vessel bifurcations in the liver, implanted gold markers in the prostate). The maximum displacements for lung,liver and prostate were 5.3 cm, 3.2 cm, and 1.8 cm respectively. The largest Registration error (direction, mean ± standard deviation) for lung,liver and prostate were (inferior‐superior, −0.21 ± 0.38 cm), (anterior‐posterior, −0.09 ± 0.34 cm), and (left‐right, 0.04 ± 0.38 cm) respectively, which was within the Image resolution regardless of the deformation model. The computation time (2.7 GHz Intel Xeon) was on the order of seconds (e.g. 10 seconds for two prostate data sets), and Image deformation results could be viewed at interactive speed (less than 1 second for 512×512 voxels). Conclusion: Surface‐based Deformable Image Registration enables the quantification of geometrical change in normal tissue and tumor with acceptable accuracy and speed.

  • SU‐FF‐J‐97: A Novel Metric for Automatic Assessment of Deformable Image Registration Accuracy
    Medical Physics, 2005
    Co-Authors: Kristy K. Brock, Joanne Moseley, D. Jaffray
    Abstract:

    Purpose: The purpose of this research is to develop a metric that automatically assesses the accuracy of Deformable Image Registration by evaluating the difference in voxel values and the minimum distance to agreement between the predicted and actual Images.Method and Materials: A metric, κ, has been developed, which is based on two parameters 1) difference in voxel value and 2) minimum distance to voxel agreement. Voxels within the predicted Image, or a selected subregion within the region, are randomly selected for evaluation. Each voxel is assessed to determine if it is within 3 Image units (i.e. Hounsfield Units or MR number) of the voxel value on the actual Image or within 0.3 cm of its corresponding voxel on the actual Image. The κ index indicates the percentage of points passing at least one of the parameter. The correlation between κ and the error in the Image was established by introducing known random error into an Image. Mathematically deformed CT and MR data was generated for analysis (mean = 0 − 0.5, SD = 0 − 1.0 cm). Results: Although the metric can overestimate the percentage of points meeting the criteria, due to similar voxel values in the search region, a unique correlation was established between the effective error in the Image with known deformation and κ. A power law relationship between described 98% of the variance between the known error and κ. This relationship was then used to assess the error in Deformable Image Registration using a finite element based method. The results show good agreement with prior manual accuracy evaluation. Conclusions: A novel metric, κ, describing the error in Deformable Image Registration has been investigated providing a unique correlation between κ and the residual error in Registration. Conflict of Interest: This research was supported in part Varian Medical Systems.