Real-time Volumetric Imaging for Motion Management and Dose Delivery Verification

用于运动管理和剂量输送验证的实时体积成像

• 批准号: 10659842
• 负责人: Zhen Tian
• 金额: $ 52.64万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-24 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659842
• 关键词: 3-Dimensional; Abdomen; Address; Adopted; Algorithms; Anatomy; Area; Articular Range of Motion; Artificial Intelligence; Biometry; Clinical; Clinical Data; Collimator; Compensation; Data; Development; Dose; Ensure; Geometry; Image; Implant; Inferior; Inhalation; Interdisciplinary Study; Linear Accelerator Radiotherapy Systems; Liver; Location; Lung; Malignant neoplasm of lung; Masks; Medical; Methods; Modernization; Monitor; Monte Carlo Method; Motion; Normal tissue morphology; Organ; Pancreas; Patients; Performance; Photons; Physicians; Physics; Plant Leaves; Radiation; Radiation Dose Unit; Radiation Oncology; Radiation therapy; Reproducibility; Respiration; Risk; Site; Supervision; System; Techniques; Technology; Testing; Three-Dimensional Image; Time; Tissues; Treatment outcome; Treatment-related toxicity; Tumor Volume; Uncertainty; X-Ray Computed Tomography; X-Ray Medical Imaging; automated segmentation; cancer radiation therapy; cancer site; cancer therapy; clinical application; clinical practice; convolutional neural network; cost; cost effective treatment; deep learning; dosimetry; falls; generative adversarial network; imaging Segmentation; improved; innovation; long short term memory; migration; novel; patient safety; radiation delivery; respiratory; simulation; success; tumor; tumor eradication

Project Summary/Abstract Stereotactic body radiation therapy (SBRT) is one of the most effective, well-tolerated, and cost-effective treatments. The success of SBRT relies heavily on the precision of dose delivery, due to the typically small tumor size, the very high radiation dose per fraction, and the sharp dose fall-off outside the target. For those sites where the tumor moves due to respiration, motion management is indispensable to ensure the high-precision dose delivery of SBRT. Current motion management strategies are either treating a large area encompassing the tumor motion range, or only delivering radiation dose within a small window (e.g., a gating window or at the end of inhale) of tumor motion cycle via indirect and inferior tumor motion monitoring (such as external surrogates or implanted fiducial markers). In-treatment real-time volumetric imaging is highly desired to enable direct, accurate, and markerless 3D tumor tracking for better motion management and capture unexpected large tumor motion for patient safety. The availability and accuracy of in-treatment real-time patient 3D anatomy information is also essential to the development of more active and advanced motion management technologies, such as multileaf collimator tracking and 4D treatment delivery. The unpredictable motion change during treatment can lead to substantial deviation of the delivered dose from the planned dose. Adaptive radiotherapy can compensate for the dosimetric errors by adapting the subsequent fractions. However, due to the notable changes of respiration, the pre-treatment imaging cannot provide the patient’s actual in-treatment anatomy to assess the actual delivered dose for adaptive radiotherapy. In-treatment real-time volumetric imaging is needed to enable dose-guided adaptive SBRT. Despite these strong needs, real-time volumetric imaging is not currently available due to the big challenge of reconstructing an instantaneous 3D image from very few 2D projections to meet the real-time requirement. To fill this clinical gap, we plan to develop a real-time volumetric imaging-based tumor tracking and dose verification (RITD) system using novel techniques in deep learning, imaging, Monte Carlo simulation and high-performance computation, and use lung SBRT treatment as a testbed. We will accomplish the following specific aims: 1) To develop and refine a real-time on-board volumetric imaging and tumor tracking method; 2) To develop an image correction method and a tumor/multi-organ segmentation method on the volumetric images; 3) To evaluate the performance of the proposed RITD system and assess its clinical benefit. The innovation of this study lies in developing new deep-learning approaches to enable real-time on-board volumetric imaging and build accurate tumor tracking and dose verification capability into cancer radiotherapy. It has substantial potential to improve lung SBRT treatment outcomes by reducing targeting uncertainty, improving treatment accuracy and precision, and enabling dose-guided adaptive lung SBRT. It paves the way for more active and advanced motion management (e.g., truly 4D radiotherapy). The proposed RITD system may be adapted for other cancer sites; thus, it has far-reaching clinical potential.

项目总结/文摘