Supporting data for "Machine-learning-based Interventional Planning and Guidance Using MR Images"

Chapter 3 comprises patient MRI samples (including prostate and rectal MR images), deep learning implementation codes, and lesion assessment software for MRI-based pre-operative cancer diagnosis and treatment planning frameworks. 

Chapter 4 includes simulation data, implementation codes, and simulation platform, in order to explore learning-based ultrasound transduction planning and control with MR thermometry and MR-ARFI guidance. This includes both ultrasound modulation and beam motion control policies. 

Chapter 5 involves CAD files, videos, and presentation files for an ultrasound transducer robotic positioner, aimed at exploring MR-based robot registration and real-time tracking for intra-operative MRI-guided interventions.

第三章涵盖患者MRI样本（包括前列腺和直肠MRI图像）、基于深度学习的实现代码以及用于MRI术前癌症诊断和治疗规划框架的病变评估软件。第四章包括模拟数据、实现代码和模拟平台，旨在探索基于学习原理的超声传输规划和控制，其中涉及MR热成像和MR-ARFI引导。这包括超声调制和光束运动控制策略。第五章涉及超声换能器机器人定位器的CAD文件、视频和演示文稿，旨在探索基于MRI的机器人配准和术中MRI引导干预的实时跟踪。