Spatio-temporal motion prediction model for liver cancer radiotherapy

自由呼吸肝癌疗法，例如外照射放射疗法 (EBRT)，需要对治疗过程中变形和移动的器官内的肿瘤进行准确跟踪。然而，肿瘤靶向干预的主要限制在于患者的呼吸或不自主运动，这可能会偏离在计划过程中根据实际解剖结构确定的预定义目标和轨迹，从而导致执行动作的治疗设备相对于目标的相对位置出现误差。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，这在介入手术中是不可行的。因此，为了完成治疗期间临床可用的部分信息（2D 图像、导航信号），需要预先了解呼吸周期期间预期运动场。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，而且避免损坏肿瘤靶点周围的关键结构。本项目将产生显着的直接和间接经济效益。至于直接好处，主要工业合作伙伴 Elekta 是放射治疗和放射外科设备和软件的创新者，将能够访问 4D-MRI 成像数据和基于机器学习的计算机化模型，这将满足他们在治疗期间运动管理的关键需求。他们与飞利浦医疗保健公司合作开发的 MR-Linac 系统的结果将特别有益，以提高瞄准精度并最大限度地减少放射期间的器官变形伪影。间接好处包括更好的患者护理和减少所需干预措施的数量。这个多学科转化研究项目将为生物医学领域的 2 名研究生、3 名本科生和 1 名博士后研究员提供独特的机会，与医学影像和病理学专家合作，并为医学生、住院医生和研究员提供将基本概念转化为临床领域的独特机会。************

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.**********