Spatio-temporal motion prediction model for liver cancer radiotherapy

肝癌放疗时空运动预测模型

• 批准号: 517413-2017
• 负责人: Kadoury, Samuel
• 金额: $ 0.96万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=697016
• 关键词: Spatio; temporal; motion; prediction; model

Free-breathing liver cancer therapies such as external beam radiation therapy (EBRT) require accurate tumor tracking within an organ which deforms and moves during treatment. However, a major limitation of tumor-targeted interventions resides in the patient's respiration or involuntary movement, which may stray the pre-defined target and trajectories determined during planning from the actual anatomy, thus inducing errors in the relative position of the therapy device performing the action with respect to the target. Live motion tracking of the internal anatomy depends on 3D imaging and image post-processing in real-time, which is unfeasible during interventional procedures. Thus, to complete partial information (2D images, navigator signal) clinically available during treatment, prior knowledge of the anticipated motion field during the breathing cycle is necessary. The project objective is to develop and evaluate a spatio-temporal liver motion model based on deep learning and biomechanical models, which will predict tumor displacement within the breathing cycle and help radiation oncologists not only track tumors, but also avoid damaging critical structures surrounding the tumor target. This project will have significant direct and indirect economic benefits. As to the direct benefits, the primary industrial Partner - Elekta - who is an innovator of equipment and software for radiation therapy and radiosurgery, will have access to 4D-MRI imaging data and computerized models based on machine learning, which will address their critical needs for motion management during therapy. The outcome will be particularly beneficial with their MR-Linac system, developed in partnership with Philips Healthcare, in order to improve targeting accuracy and minimize organ deformation artefacts during radiation. Indirect benefits include better patient care and reduced number of required interventions. This multidisciplinary translational research program will provide a unique opportunity for 2 graduate students, 3 undergraduate students and one post-doctoral fellow in the field of biomedical sciences to work with experts in medical imaging and pathology and for medical students, residents and fellows to translate fundamental concepts in the clinical field.

自由呼吸的肝癌疗法，例如外束辐射疗法（EBRT），需要在器官内进行准确的肿瘤跟踪，该器官会在治疗过程中变形和移动。然而，靶向肿瘤的干预措施的主要局限性在于患者的呼吸或非自愿运动，这可能会导致从实际解剖结构进行计划期间确定的预定义的靶标和轨迹，从而在治疗设备的相对位置中造成对目标作用的相对位置的错误。内部解剖结构的实时运动跟踪取决于3D成像和实时后处理，这在介入过程中是不可行的。因此，为了完成治疗期间临床上可用的部分信息（2D图像，导航器信号），需要在呼吸周期中对预期运动场的先验知识。该项目的目标是基于深度学习和生物力学模型开发和评估一个时空肝运动模型，该模型将预测呼吸周期内的肿瘤位移，并帮助辐射肿瘤学家不仅追踪肿瘤，而且还要避免损害周围肿瘤靶标周围的关键结构。该项目将具有巨大的直接和间接经济利益。至于直接收益，主要工业合作伙伴Elekta-是用于放射治疗和放射外科手术的设备和软件的创新者，将访问基于机器学习的4D -MRI成像数据和计算机模型，这将满足其在治疗期间对运动管理的关键需求。与Philips Healthcare合作开发的MR-LINAC系统将特别有益，以提高靶向准确性并最大程度地减少辐射过程中器官变形伪像。间接益处包括更好的患者护理和减少所需干预措施的数量。这项多学科翻译研究计划将为2名研究生，3名本科生和一名生物医学科学领域的博士后研究员提供独特的机会，以与医学成像和病理学专家以及医学生，居民，居民和研究员合作，以在临床领域转化基本概念。