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M Sharpe - One of the best experts on this subject based on the ideXlab platform.
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predicting objective function weights from Patient Anatomy in prostate imrt treatment planning
Medical Physics, 2013Co-Authors: M Hammad, Timothy J Craig, Timothy C Y Chan, M SharpeAbstract:Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from Patient Anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 Patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between Patients. A regression model was developed to predict a Patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weightsmore » across all Patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all Patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one Patient. Conclusions: This study demonstrates a proof of concept that Patient Anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore.« less
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su e t 653 predicting objective function weights for imrt prostate treatment planning using Patient Anatomy
Medical Physics, 2013Co-Authors: M Hammad, Tcy Chan, Timothy J Craig, M SharpeAbstract:Purpose: To develop a prediction model for objective function weights for intensity‐modulated radiation therapy (IMRT) prostate treatment planning with multiple objectives using geometry information from Patient Anatomy. Methods: A previously developed inverse optimization method (IOM) was used to reverse‐engineer optimal objective function weights (inverse weights) from an observed treatment plan. We developed a regression model to predict the weights for IMRT prostate treatment planning using Patient Anatomy from 25 Patients. The ratio of the overlap volumes of the rectum and bladder with the planning target volume expanded by 1cm was used to predict the bladder and rectum weights. The femoral head weights were included in the model as a small fixed weight (1%). The model was validated using leave‐one‐out cross‐validation. We evaluated the model by comparing the treatment plans generated through inverse planning using the inverse weights from IOM and the predicted weights from the regression model. Results: On average, V54Gy for the bladder was 36.1% using the inverse weights and 36.6% using the predicted weights. V70Gy for the bladder was 23.2% (inverse) and 23.5% (predicted). For the rectum, V54Gy was 34.6% (inverse) and 33.9% (predicted), and V70Gy was 22.6% (inverse) and 22.3% (predicted). For each criterion, the difference between the inverse and predicted metrics was not statistically significant. All treatment plans from the predicted weights satisfied the clinical criteria. Conclusion: Our results show that objective function weights are well‐predicted by the regression model. This approach may support the genesis of personalized weights in IMRT treatment planning. This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and Ontario Graduate Scholarship (OGS).
M Hammad - One of the best experts on this subject based on the ideXlab platform.
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predicting objective function weights from Patient Anatomy in prostate imrt treatment planning
Medical Physics, 2013Co-Authors: M Hammad, Timothy J Craig, Timothy C Y Chan, M SharpeAbstract:Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from Patient Anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 Patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between Patients. A regression model was developed to predict a Patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weightsmore » across all Patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all Patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one Patient. Conclusions: This study demonstrates a proof of concept that Patient Anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore.« less
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su e t 653 predicting objective function weights for imrt prostate treatment planning using Patient Anatomy
Medical Physics, 2013Co-Authors: M Hammad, Tcy Chan, Timothy J Craig, M SharpeAbstract:Purpose: To develop a prediction model for objective function weights for intensity‐modulated radiation therapy (IMRT) prostate treatment planning with multiple objectives using geometry information from Patient Anatomy. Methods: A previously developed inverse optimization method (IOM) was used to reverse‐engineer optimal objective function weights (inverse weights) from an observed treatment plan. We developed a regression model to predict the weights for IMRT prostate treatment planning using Patient Anatomy from 25 Patients. The ratio of the overlap volumes of the rectum and bladder with the planning target volume expanded by 1cm was used to predict the bladder and rectum weights. The femoral head weights were included in the model as a small fixed weight (1%). The model was validated using leave‐one‐out cross‐validation. We evaluated the model by comparing the treatment plans generated through inverse planning using the inverse weights from IOM and the predicted weights from the regression model. Results: On average, V54Gy for the bladder was 36.1% using the inverse weights and 36.6% using the predicted weights. V70Gy for the bladder was 23.2% (inverse) and 23.5% (predicted). For the rectum, V54Gy was 34.6% (inverse) and 33.9% (predicted), and V70Gy was 22.6% (inverse) and 22.3% (predicted). For each criterion, the difference between the inverse and predicted metrics was not statistically significant. All treatment plans from the predicted weights satisfied the clinical criteria. Conclusion: Our results show that objective function weights are well‐predicted by the regression model. This approach may support the genesis of personalized weights in IMRT treatment planning. This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and Ontario Graduate Scholarship (OGS).
J Wolfgang - One of the best experts on this subject based on the ideXlab platform.
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su ff j 106 volumetric visualization of clinical contours dose high definition Patient Anatomy for four dimensional adaptive radiotherapy treatment planning
Medical Physics, 2009Co-Authors: J Hallman, G Sharp, G Chen, J WolfgangAbstract:Purpose: Volume rendered analysis of organ motion throughout the course of radiotherapy, investigating organ interplay during inter‐ and intra‐fraction motion and Patient variation. Method and Materials: We used a two‐step process to volumetrically visualize clinical contours and dose alongside Patient Anatomy. First, deformable image registration methods were applied to warp physician‐drawn contours from a single phase (either end‐of‐exhale or mid‐exhale) to all other respiratory phases of a four‐dimensional treatment dataset. For this process, a b‐spline, intensity‐matching algorithm was used to deform the original physician‐drawn contours. The resultant contours were then fused onto their respective phases. Next, a High Definition® Volume Rendering engine by Fovia© Inc. was used to render and display structure contours alongside dose distributions and high‐detail Anatomy. The four‐dimensional display allowed for dynamic, interactive, and intuitive visualization and qualitative assessment of treatment accuracy. A set of sample thoracic, GI, and head and neck cases were reviewed following this methodology. In addition to dose‐delivery verification, anatomical regions with reported treatment complications were scrutinized for any unusual dose patterns or organ movements/deformations. Results: Our results show agreement between the centers of mass and targets identified automatically by our deformable image registration and manually by the radiation oncologist. Both qualitative and quantitative analyses provided useful insights into the potential and limitations of four‐dimensional radiotherapytreatment planning.Conclusion: Time variation of Patient physiology during radiotherapy warrants continued reassessment of the delivered treatment plan. Volume rendering allows for improved visualization of the interplay between planned treatment ports and Patient Anatomy, illustrating in an interactive way changes as they occur over the course of treatment.
Parvin Mousavi - One of the best experts on this subject based on the ideXlab platform.
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An augmented reality haptic training simulator for spinal needle procedures
IEEE Transactions on Biomedical Engineering, 2013Co-Authors: Colin Sutherland, Rick Sellens, Keyvan Hashtrudi-zaad, Purang Abolmaesumi, Parvin MousaviAbstract:This paper presents the prototype for an augmented reality haptic simulation system with potential for spinal needle insertion training. The proposed system is composed of a torso mannequin, a MicronTracker2 optical tracking system, a PHANToM haptic device, and a graphical user interface to provide visual feedback. The system allows users to perform simulated needle insertions on a physical mannequin overlaid with an augmented reality cutaway of Patient Anatomy. A tissue model based on a finite-element model provides force during the insertion. The system allows for training without the need for the presence of a trained clinician or access to live Patients or cadavers. A pilot user study demonstrates the potential and functionality of the system.
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Medical Imaging: Image-Guided Procedures - Ultrasound guided spine needle insertion
Proceedings of SPIE, 2010Co-Authors: Elvis C. S. Chen, Parvin Mousavi, Sean Gill, Gabor Fichtinger, Purang AbolmaesumiAbstract:An ultrasound (US) guided, CT augmented, spine needle insertion navigational system is introduced. The system consists of an electromagnetic (EM) sensor, an US machine, and a preoperative CT volume of the Patient Anatomy. Three-dimensional (3D) US volume is reconstructed intraoperatively from a set of two-dimensional (2D) freehand US slices, and is coregistered with the preoperative CT. This allows the preoperative CT volume to be used in the intraoperative clinical coordinate. The spatial relationship between the Patient Anatomy, surgical tools, and the US transducer are tracked using the EM sensor, and are displayed with respect to the CT volume. The pose of the US transducer is used to interpolate the CT volume, providing the physician with a 2D "x-ray vision" to guide the needle insertion. Many of the system software components are GPU-accelerated, allowing real-time performance of the guidance system in a clinical setting.
Ron Alterovitz - One of the best experts on this subject based on the ideXlab platform.
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IROS - Planning High-Quality Motions for Concentric Tube Robots in Point Clouds via Parallel Sampling and optimization
Proceedings of the ... IEEE RSJ International Conference on Intelligent Robots and Systems. IEEE RSJ International Conference on Intelligent Robots an, 2019Co-Authors: Alan Kuntz, Mengyu Fu, Ron AlterovitzAbstract:We present a method that plans motions for a concentric tube robot to automatically reach surgical targets inside the body while avoiding obstacles, where the Patient’s Anatomy is represented by point clouds. Point clouds can be generated intra-operatively via endoscopic instruments, enabling the system to update obstacle representations over time as the Patient Anatomy changes during surgery. Our new motion planning method uses a combination of sampling-based motion planning methods and local optimization to efficiently handle point cloud data and quickly compute high quality plans. The local optimization step uses an interior point optimization method, ensuring that the computed plan is feasible and avoids obstacles at every iteration. This enables the motion planner to run in an anytime fashion, i.e., the method can be stopped at any time and the best solution found up until that point is returned. We demonstrate the method’s efficacy in three anatomical scenarios, including two generated from endoscopic videos of real Patient Anatomy.
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Planning High-Quality Motions for Concentric Tube Robots in Point Clouds via Parallel Sampling and optimization
2019 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2019Co-Authors: Alan Kuntz, Mengyu Fu, Ron AlterovitzAbstract:We present a method that plans motions for a concentric tube robot to automatically reach surgical targets inside the body while avoiding obstacles, where the Patient's Anatomy is represented by point clouds. Point clouds can be generated intra-operatively via endoscopic instruments, enabling the system to update obstacle representations over time as the Patient Anatomy changes during surgery. Our new motion planning method uses a combination of sampling-based motion planning methods and local optimization to efficiently handle point cloud data and quickly compute high quality plans. The local optimization step uses an interior point optimization method, ensuring that the computed plan is feasible and avoids obstacles at every iteration. This enables the motion planner to run in an anytime fashion, i.e., the method can be stopped at any time and the best solution found up until that point is returned. We demonstrate the method's efficacy in three anatomical scenarios, including two generated from endoscopic videos of real Patient Anatomy.