Spatio-temporal motion prediction model for liver cancer radiotherapy

自由呼吸肝癌治疗，如外部束放射治疗（EBRT），需要在治疗过程中变形和移动的器官内精确跟踪肿瘤。然而，肿瘤靶向干预的一个主要限制在于患者的呼吸或不自主运动，这可能会偏离预定的目标和在计划过程中确定的轨迹，偏离实际解剖结构，从而导致治疗装置相对于目标执行动作的相对位置错误。内部解剖的实时运动跟踪依赖于三维成像和实时图像后处理，这在介入手术中是不可行的。因此，为了在治疗期间完成临床可用的部分信息（2D图像、导航信号），必须事先了解呼吸周期中预期的运动场。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，帮助放射肿瘤学家不仅跟踪肿瘤，而且避免损伤肿瘤靶点周围的关键结构。该工程将产生显著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，将获得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。结果将特别有利于他们的MR-Linac系统，该系统是与飞利浦医疗保健合作开发的，以提高靶向精度并最大限度地减少辐射期间的器官变形。间接效益包括更好的病人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名博士后提供一个独特的机会，与医学成像和病理学专家一起工作，并为医科学生、住院医生和研究员提供一个在临床领域转化基本概念的机会。**********

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.**********