A Multi-DOF Soft Robot Mechanism for Patient Motion Correction and Beam Orientation Selection in Cancer Radiation Therapy




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Accurate patient immobilization in conformal radiation therapy is crucial for efficient cancer treatment. Good treatment outcomes require accurate patient immobilization and a good choice of beam orientations. State-of-the-art immobilization systems rely on metallic or rigid masks which lack morphological properties, attenuate ionizing radiation, degrade dose efficacy, and are uncomfortable for the patient during treatment. The de-facto open-loop and deferred positioning procedures sometimes cause eczema or brain damage. We synthesize system identification, finite elastic deformation, and control systems to harness soft robot mechanisms for real-time motion correction in cancer radiation therapy scenarios. Additionally, in most inverse treatment planning schemes today, the “right” beam angles among the myriad possibilities in beam space are usually determined through intuition and experience by treatment planners in a time-consuming trial-and-error procedure. Existing mathematical optimization techniques fail to meet a (near) real-time planning requirement. We propose a supervised pre-training of a deep neural network to assure quality beam plans are predicted in a real-time feasible manner. Our approach has the advantage of predicting feasible beam angles in near real time, and it is adaptable to treatment modalities that require large beam plans, and 4π-noncoplanar radiation therapy such as VMAT.



Continuum mechanics, Soft condensed matter, System identification, Adaptive control systems, Reinforcement learning, Cancer--Radiotherapy, Robotics in medicine



©2019 Olalekan Patrick Ogunmolu. All rights reserved.