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: $ 3.29万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689177
• 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.

自由呼吸肝癌治疗，如体外放射治疗（EBRT），需要在治疗期间变形和移动的器官内进行准确的肿瘤跟踪。然而，肿瘤靶向介入的主要限制在于患者的呼吸或无意识运动，这可能使预定义目标和在规划期间确定的轨迹偏离实际解剖结构，从而引起执行动作的治疗设备相对于目标的相对位置的误差。内部解剖结构的实时运动跟踪依赖于实时的3D成像和图像后处理，这在介入手术期间是不可行的。因此，为了在治疗期间完成临床可用的部分信息（2D图像、导航器信号），呼吸周期期间预期运动场的先验知识是必要的。该项目的目标是开发和评估基于深度学习和生物力学模型的时空肝脏运动模型，该模型将预测呼吸周期内的肿瘤位移，并帮助放射肿瘤学家不仅跟踪肿瘤，还避免损伤肿瘤靶周围的关键结构。该项目将产生巨大的直接和间接经济效益。至于直接利益，主要的工业合作伙伴- Elekta -是放射治疗和放射外科设备和软件的创新者，将可以访问4D-MRI成像数据和基于机器学习的计算机化模型，这将解决他们在治疗期间运动管理的关键需求。这一结果将特别有利于与飞利浦医疗保健合作开发的MR-Linac系统，以提高靶向准确性并最大限度地减少辐射期间的器官变形伪影。间接的好处包括更好的病人护理和减少所需的干预措施。这个多学科的翻译研究计划将提供一个独特的机会，为2名研究生，3名本科生和一名博士后研究员在生物医学科学领域与医学成像和病理学专家合作，并为医学生，居民和研究员翻译临床领域的基本概念。