A Model-Based Multi-Point Tissue Manipulation for Enhancing Breast Brachytherapy

In surgical operations, tissue manipulation can be automated to reduce the surgeon’s workload. This work addresses the application of tissue manipulation in breast brachytherapy, which involves manipulating an internal target inside the breast. Unassisted breast brachytherapy causes excessive target movement that reduces seed implantation accuracy. To address this target movement in breast brachytherapy, first, the internal target point will be manipulated accurately and then the brachytherapy needle will be inserted into the immobilized tissue. In this paper, a model-based tissue manipulation method is introduced. To simulate nonlinear large tissue deformation for the first time, a minimum-energy-based deformable tissue solver is utilized. Based on the theory of positive bases, the optimal number of actuators is determined to guarantee controllability of the internal target. A model predictive controller (MPC) is designed to implement multi-point tissue manipulation. A breast phantom is used to test the accuracy of the deformation model and the effectiveness of the proposed control method. The results show that the tissue deformation simulation error is 1.6 mm and the internal target can be regulated with negligible steady-state errors using an MPC controller.