Q Gautier

Dosimetric benefit of adaptive re-planning in pancreatic cancer stereotactic body radiotherapy

Stereotactic body radiotherapy (SBRT) shows promise in unresectable pancreatic cancer, though this treatment modality has high rates of normal tissue toxicity. This study explores the dosimetric utility of daily adaptive re-planning with pancreas SBRT. We used a previously developed supercomputing online re-planning environment (SCORE) to re-plan 10 patients with pancreas SBRT. Tumor and normal tissue contours were deformed from treatment planning computed tomographies (CTs) and transferred to daily cone-beam CT (CBCT) scans before re-optimizing each daily treatment plan. We compared the intended radiation dose, the actual radiation dose, and the optimized radiation dose for the pancreas tumor planning target volume (PTV) and the duodenum. Treatment re-optimization improved coverage of the PTV and reduced dose to the duodenum. Within the PTV, the actual hot spot (volume receiving 110% of the prescription dose) decreased from 4.5% to 0.5% after daily adaptive re-planning. Within the duodenum, the volume receiving the prescription dose decreased from 0.9% to 0.3% after re-planning. It is noteworthy that variation in the amount of air within a patient׳s stomach substantially changed dose to the PTV. Adaptive re-planning with pancreas SBRT has the ability to improve dose to the tumor and decrease dose to the nearby duodenum, thereby reducing the risk of toxicity.

An Automated Treatment Plan Quality Control Tool for Intensity-Modulated Radiation Therapy Using a Voxel-Weighting Factor-Based Re-Optimization Algorithm

Intensity-modulated radiation therapy (IMRT) currently plays an important role in radiotherapy, but its treatment plan quality can vary significantly among institutions and planners. Treatment plan quality control (QC) is a necessary component for individual clinics to ensure that patients receive treatments with high therapeutic gain ratios. The voxel-weighting factor-based plan re-optimization mechanism has been proved able to explore a larger Pareto surface (solution domain) and therefore increase the possibility of finding an optimal treatment plan. In this study, we incorporated additional modules into an in-house developed voxel weighting factor-based re-optimization algorithm, which was enhanced as a highly automated and accurate IMRT plan QC tool (TPS-QC tool). After importing an under-assessment plan, the TPS-QC tool was able to generate a QC report within 2 minutes. This QC report contains the plan quality determination as well as information supporting the determination. Finally, the IMRT plan quality can be controlled by approving quality-passed plans and replacing quality-failed plans using the TPS-QC tool. The feasibility and accuracy of the proposed TPS-QC tool were evaluated using 25 clinically approved cervical cancer patient IMRT plans and 5 manually created poor-quality IMRT plans. The results showed high consistency between the QC report quality determinations and the actual plan quality. In the 25 clinically approved cases that the TPS-QC tool identified as passed, a greater difference could be observed for dosimetric endpoints for organs at risk (OAR) than for planning target volume (PTV), implying that better dose sparing could be achieved in OAR than in PTV. In addition, the dose-volume histogram (DVH) curves of the TPS-QC tool re-optimized plans satisfied the dosimetric criteria more frequently than did the under-assessment plans. In addition, the criteria for unsatisfied dosimetric endpoints in the 5 poor-quality plans could typically be satisfied when the TPS-QC tool generated re-optimized plans without sacrificing other dosimetric endpoints. In addition to its feasibility and accuracy, the proposed TPS-QC tool is also user-friendly and easy to operate, both of which are necessary characteristics for clinical use.

TH‐C‐137‐10: Development of a GPU Research Platform for Automatic Treatment Planning and Adaptive Radiotherapy Re‐Planning

PURPOSE To develop a research platform called SCORE (Super Computing Online Re-planning Environment) for automatic treatment planning and adaptive radiotherapy re-planning based on GPU. METHODS Our software is a Graphical User Interface (GUI) based on the Qt framework that allows users to easily and quickly create a new treatment plan based on a reference plan. It consists of several modules, including loading plan and patient geometry from DICOM RT files, automatic and manual rigid registration, deformable registration for contour propagation, previous plan based automatic plan optimization, physician-driven plan tuning, final dose calculation, and plan exporting in DICOM RT format. For automatic planning, a reference plan is identified from a library of previously delivered plans and it is used to guide the optimization process. For adaptive radiotherapy re-planning, the original plan of the same patient is used as the reference plan to guide the optimization process to generate a new plan on the new patient geometry defined by either a new CT or Cone-Beam CT image. All the computation modules have been implemented in CUDA to achieve high efficiency. The results for each step of the workflow can be visualized for review, revision, and approval. RESULTS SCORE has been well tested and validated for prostate and head/neck cancer cases. The validation was done by comparing SCORE plans against the same plans with re-calculated dose distributions and DVHs using a commercial planning system. We found that SCORE can generate clinically optimal treatment plans that are realistic and deliverable. The plans can be automatically created in only a few minutes, followed by another few minutes of physician fine-tuning using an interactive GUI. CONCLUSION We have developed a very efficient and user-friendly GPU-based research platform that can be used for clinical research on automatic treatment planning and adaptive radiotherapy re-planning.

Adaptive Threshold Non-Pareto Elimination: Re-thinking machine learning for system level design space exploration on FPGAs

One major bottleneck of the system level OpenCL-to-FPGA design tools is their extremely time consuming synthesis process (including place and route). The design space for a typical OpenCL application contains thousands of possible designs even when considering a small number of design space parameters. It costs months of compute time to synthesize all these possible designs into end-to-end FPGA implementations. Thus, the brute force design space exploration (DSE) is impractical for these design tools. Machine learning is one solution that identifies the valuable Pareto designs by sampling only a small portion of the entire design space. However, most of the existing machine learning frameworks focus on improving the design objective regression accuracy, which is not necessarily suitable for the FPGA DSE task. To address this issue, we propose a novel strategy - Adaptive Threshold Non-Pareto Elimination (ATNE). Instead of focusing on regression accuracy improvement, ATNE focuses on understanding and estimating the inaccuracy. ATNE provides a Pareto identification threshold that adapts to the estimated inaccuracy of the regressor. This adaptive threshold results in a more efficient DSE. For the same prediction quality, ATNE reduces the synthesis complexity by 1.6 - 2.89× (hundreds of synthesis hours) against the other state of the art frameworks for FPGA DSE. In addition, ATNE is capable of identifying the Pareto designs for certain difficult design spaces which the other existing frameworks are incapable of exploring effectively.

Spector: An OpenCL FPGA benchmark suite

High-level synthesis tools allow programmers to use OpenCL to create FPGA designs. Unfortunately, these tools have a complex compilation process that can take several hours to synthesize a single design. This creates a significant barrier for design optimization since even experts typically need to test many designs due to the non-obvious interactions between the different optimizations. Thus, understanding the design space, and guiding the optimization process is a crucial requirement for enabling the widespread adoption of these high-level synthesis tools. However this requires a significant amount of design space data that is currently unavailable or difficult to generate. To solve this problem, we present an OpenCL FPGA benchmark suite. We outfitted each benchmark with a range of optimization parameters (or knobs), compiled over 8300 unique designs using the Altera OpenCL SDK, executed them on a Terasic DE5 board, and recorded their corresponding performance and utilization characteristics. We describe the resulting design spaces, and perform a statistical analysis of the optimization configurations which provides valuable architecture insights to FPGA developers. We make the benchmarks and results completely open-source to give opportunities for the community to perform additional analyses and provide a repository of well-documented designs for follow-on research.

Real-time 3D reconstruction for FPGAs: A case study for evaluating the performance, area, and programmability trade-offs of the Altera OpenCL SDK

Embedding real-time 3D reconstruction of a scene from a low-cost depth sensor can improve the development of technologies in the domains of augmented reality, mobile robotics, and more. However, current implementations require a computer with a powerful GPU, which limits its prospective applications with low-power requirements. To implement low-power 3D reconstruction we embedded two prominent algorithms of 3D reconstruction (Iterative Closest Point and Volumetric Integration) on an Altera Stratix V FPGA by using the OpenCL language and the Altera OpenCL SDK. In this paper, we present our application and evaluation of the Altera tool in terms of performance, area, and programmability trade-offs. We have verified that OpenCL can be a viable method for developing FPGA applications by modifying an open-source version of the Microsoft KinectFusion project to run partially on a FPGA.

SU‐E‐T‐718: Automatic IMRT Plan Quality Control for GYN Cancer Clinical Trials

Purpose: To perform efficient quality control (QC) of intensity‐modulated radiotherapy (IMRT) plans of gynecological (GYN) cancer in clinical trials. Methods: Plan QC is a necessary component in clinical trials that requires much time and effort. IMRT plans from the INTERTECC clinical trial for the treatment of intact cervix cancer patients have been used to evaluate a plan QC tool. The CT image, organ and target contours, and the clinical IMRT plan were imported into a GPU‐based treatment re‐planning system that was originally designed to perform online adaptive radiotherapy. The clinical plan was considered as the reference plan and the re‐planning system automatically created a new plan. The objective for re‐planning was to reduce dose of organs‐at‐risk (OARs) and to get a more uniform dose in the PTV with respect to the reference plan. After performing automatic re‐planning on each plan, we compared the new and the reference DVH curves in terms of the treatment planning goals specified in the clinical trial protocol. Results: Data from 12 patients was retrospectively analyzed. Results showed that DVH curves of the automatic plan satisfy the specified clinical constraints more frequently than the actual clinical plans. DVH for the PTV of the new plan was in most cases similar to the clinical plans, indicating good quality PTV coverage. In 2 out of the 12 cases the QC tool could achieve a PTV hotspots reduction of 11% and 10% respectively. For most patients, lower doses in OARs were achieved after re‐planning, except in those plans where there was overlap between OARs and PTV. Re‐planning takes less than 1 min for each case. Conclusion: We have proved the feasibility of efficient IMRT plan QC in clinical trials for patients with GYN cancer. Varian Medical Systems through a Master Research Agreement

SU-E-T-242: A Gateway for GPU Computations in Radiotherapy

Purpose: Graphics Processing Unit (GPU) has become increasingly important in radiotherapy. However, it is still difficult for general clinical researchers to access GPU codes developed by other researchers with GPU expertise, and also for developers to benchmark their codes. It is quite often to see repeated efforts spent on developing GPU codes with low quality. The goal of this project is to establish an infrastructure for sharing GPU codes in the community. Methods: We deployed a GPU code sharing infrastructure on a GPU cluster. A number of codes developed in our group can be accessed via a web interface. To use the services, researchers first upload their test data or use the data provided by our system. Then they have to select the GPU device they are going to run the codes. Our system offers all mainstream GPU hardware for code benchmarking purpose. After the code running is complete, the system will automatically summarize and display the computing results. We will also release a SDK to allow the developers to build their own algorithm implementation and submit their binary codes to the system. The submitted code will be systematically benchmarked using a variety of GPU hardware and representative clinical data provided by our system. Results: This project provides a platform to the public to access a variety of GPU codes for radiotherapy research via a web interface. With the help of this platform, researchers are able to focus their efforts on clinical research. Developers will also benefit from this platform by benchmarking their codes on various GPU platforms and clinical data sets and comparing with other peoples codes for the same application. Conclusion: The gateway for GPU code developers and clinical research users can greatly facilitate the adoption of GPU codes in radiotherapy.

MO‐A‐137‐10: Evaluation of A GPU‐Based In‐House Automatic Re‐Planning System for Adaptive Radiotherapy Re‐Planning for Head and Neck Cancer

PURPOSE To evaluate the feasibility and quality of adaptive radiation therapy (ART) re-planning for head-and-neck (H/N) cancer using SCORE, an in-house graphics processing unit (GPU)-based automatic re-planning system. METHODS Our in-house automatic re-planning system SCORE utilizes GPU computational efficiency to register CT images, deform contours, and re-optimize treatment plans. We used SCORE to generate two re-plans for ten previously-treated bilateral H/N patients who were re-planned during the course of their IMRT treatment. The first plan (AUTOCONTOUR&AUTOPLAN) employed the full automatic replanning procedure while the second plan (AUTOPLAN) used the approved manually re-contoured structures from the clinically treated re-plan for SCORE optimization. To evaluate SCORE performance in terms of plan quality, both with and without human intervention in the contouring process, we compared the two SCORE plans to the clinically-delivered plan. RESULTS We compared dose volume histograms along with max/mean organ dose for each plan to the clinically treated re-plan. For AUTOCONTOUR&AUTOPLAN, we cast the manually re-contoured structures over the dose distribution to be able to compare dose on the same contours. Averaging over 10 patients, AUTOPLAN&AUTOCONTOUR reduced max brainstem, max cord, mean left parotid, and mean right parotid relative dose by 17.2(SD:12.4)%, 3.5(SD:6.3)%, 9.1(SD:6.5)%, and 9.6(SD:6.4)%, respectively, but also decreased high dose PTV coverage with a relative dose difference of - 10.3(SD:3.83)% at 99% of the volume. AUTOPLAN reduced max brainstem, max cord, mean left parotid, and mean right parotid relative dose by 22.7(SD:9.2)%, 10.8(SD:7.8)%, 9.1(SD:8.0)%, and 5.5(SD:5.8)%, respectively, while the high-dose PTV at 99% of volume increased by 0.1(SD:1.6)% and volume greater than 110% of the dose increased by 5.7(SD:6.5)%. CONCLUSION We found the automated AUTOCONTOUR&AUTOPLAN demonstrated organ sparing improvements, but decreased PTV coverage. The partially automated AUTOPLAN showed significant dosimetric improvements in terms of both organ sparing and PTV coverage but yielded plans that may be considered clinically hot.

SU‐E‐T‐680: An Interactive Graphical User Interface for Physician‐Driven Treatment Plan Tuning

PURPOSE To develop an interactive graphical user interface (GUI) for physicians to conveniently and efficiently fine-tune an IMRT treatment plan. METHODS After an IMRT plan is developed either automatically or manually by a dosimetrist, the attending physician evaluates its quality and prefer to directly modify the DVH curves and the unsatisfactory part of dose distribution. We have developed an interactive GUI for this purpose. For the unsatisfactory part of a DVH curve, the physician can drag and drop it to where it is preferred using the computer mouse or touchpad. The treatment plan is then re-optimized in near real-time on the background GPUs to best match the physicians desire. The physician can also click a point in the dose distribution and drag it to modify the isodose lines to a more desired configuration. This mouse movement vector is propagated to its neighboring area to form a local vector field, which is used to modulate the underlying dose distribution. The new dose distribution is used to guide the plan re-optimization in near real-time. The re-optimized DVHs and isodose lines are then displayed for the physicians to edit in the next iteration. This process is repeated until a physician satisfactory plan is achieved. RESULTS We have tested this GUI for a series of IMRT plans. Results indicate that the proposed method provides the physicians an intuitive and efficient graphical tool to edit the DVHs and dose distributions according to their preference. The input information is used to guide plan re-optimization in near real-time using our GPU optimization engine. Typically, a satisfactory plan can be developed by a physician in a few minutes using this tool. CONCLUSION We have developed an interactive GUI for tuning IMRT treatment plans and demonstrated its feasibility through a series of clinical tests.