Improved Techniques for Substitute CT Generation from MRI datasets

从 MRI 数据集生成替代 CT 的改进技术

• 批准号: 9927625
• 负责人: Alan Blair McMillan
• 金额: $ 45.89万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-10 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9927625
• 关键词: 3-Dimensional; Abdomen; Air; Algorithms; Area; Body Regions; Brain; Chest; Clinical; Data; Data Set; Databases; Deformity; Development; Evaluation; Financial compensation; Future; Generations; Head; Head and neck structure; Image; Ionizing radiation; Linear Accelerator Radiotherapy Systems; Machine Learning; Magnetic Resonance Imaging; Measurement; Methodology; Methods; Motion; PET/CT scan; Pathologic; Patients; Pelvis; Performance; Positron-Emission Tomography; Psychological Transfer; Radial; Radiation exposure; Radiation therapy; Residual state; Resolution; Sampling; Techniques; Technology; Tissues; Training; Uncertainty; Work; X-Ray Computed Tomography; attenuation; base; bone; convolutional neural network; deep learning; electron density; image guided; imaging capabilities; improved; learning network; prospective; real-time images; reconstruction; respiratory; routine imaging; simulation; soft tissue; treatment planning; tumor; whole body imaging

This proposal will enable improved substitute CT images for use in PET/MR and MR-only radiation treatment planning. Given the greatly improved soft-tissue contrast of MR relative to CT, which aids interpretation of PET for PET/MR and target delineation for radiation treatment planning, a remaining limitation is the current capability to obtain sufficiently accurate substitute CT images from only MR-data. Unfortunately, MRI has limited capability to resolve bone and the inability of most MR acquisitions to distinguish between air and bone makes segmentation of these tissues types challenging. This project will utilize deep learning, a new and growing area of machine learning, to develop new methodology to create substitute CT images from rapid MR acquisitions that can be utilized in PET/MR and radiation treatment planning workflows. In Aim 1 we will study rapid MR acquisitions to be used with deep learning approaches for sCT generation in the head and pelvis using 3T PET/MR images matched with PET/CT imaging to create deep learning training and evaluation datasets. Different deep learning networks and MR inputs will be studied and adapted to determine the best PET reconstruction performance. In Aim 2 we will investigate rapid but motion-resilient approaches to whole- body MR imaging for subsequent deep learning-based substitute CT generation. In an exploratory subaim, we also propose to study methods of sCT generation that only utilize PET-only data. The data acquired in Aim 2 will be used to create comprehensive whole-body, motion-resilient datasets for training and evaluation of deep learning networks. In Aim 3 we will evaluate substitute CT approaches for MR-only radiation treatment planning. MR-only approaches will be compared to standard CT-based treatment simulation in the brain, head & neck, chest, abdomen, and pelvis and deep learning networks will be optimized and evaluated for region- specific RT planning and simulation. Additionally, transfer learning approaches will be studied to extend sCT to a 0.35T MR-Linac to demonstrate respiratory motion resolved substitute CT generation.

该建议将使改进的替代CT图像用于PET/MR和MR辐射处理 规划。鉴于MR相对于CT的软组织对比度大大改善，这有助于解释PET 对于PET/MR和目标描述用于放射治疗计划，剩余的限制是当前的 仅从MR-DATA获得足够精确的替代CT图像的能力。不幸的是，MRI有 解决骨骼的能力有限，大多数MR采购无法区分空气和骨骼 使这些组织类型的分割具有挑战性。该项目将利用深度学习，一个新的和 机器学习的增长领域，开发新方法来创建Rapid MR的替代CT图像 可用于PET/MR和放射治疗计划工作流程中的收购。在目标1中，我们将学习 快速MR采集将与深度学习方法一起用于头部和骨盆中的SCT生成 使用与PET/CT成像匹配的3T PET/MR图像来创建深度学习训练和评估 数据集。将研究和适应不同的深度学习网络和MR输入以确定最佳 宠物重建性能。在AIM 2中，我们将调查快速但动作的方法 Body MR Imaging用于随后的深度学习替代CT生成。在探索性的Subiaim中，我们 还建议研究仅利用纯宠物数据的SCT生成方法。在AIM 2中获取的数据 将用于创建全面的全身，运动弹性数据集，以培训和评估深度 学习网络。在AIM 3中，我们将评估替代CT方法的仅MR辐射处理 规划。将MR唯一方法与大脑中的标准基于CT的治疗模拟进行比较 ＆颈部，胸部，腹部和骨盆以及深度学习网络将被优化和评估。 特定的RT计划和模拟。此外，将研究转移学习方法以将SCT扩展到 0.35T MR-LINAC，可证明呼吸运动分辨替代CT生成。