Improved Techniques for Substitute CT Generation from MRI datasets

这项提议将使改进的替代CT图像用于PET/MR和仅限MR的放射治疗 计划。考虑到相对于CT，MR的软组织对比度有很大的改善，这有助于解释PET 对于PET/MR和用于放射治疗计划的目标描绘，剩余的限制是当前 仅从MR数据获得足够准确的替代CT图像的能力。不幸的是，核磁共振有 分辨骨骼的能力有限，而且大多数MR成像无法区分空气和骨骼 这使得这些组织类型的分割具有挑战性。该项目将利用深度学习，一种新的和 不断增长的机器学习领域，开发新的方法来从快速磁共振创建替代CT图像 可用于正电子发射计算机断层扫描/磁共振和放射治疗计划工作流程的收购。在目标1中，我们将研究 用于头部和骨盆SCT生成的深度学习方法的快速MR采集 使用3T PET/MR图像与PET/CT成像相匹配创建深度学习训练和评估 数据集。将对不同的深度学习网络和MR输入进行研究和调整，以确定最佳 宠物重建表演。在目标2中，我们将研究快速但具有运动弹性的方法来整体- 体部磁共振成像，用于后续基于深度学习的替代CT生成。在一个探索性的子目标中，我们 还建议研究仅使用PET数据的SCT生成方法。在目标2中获得的数据 将用于创建全面的全身、具有运动弹性的数据集，用于训练和评估深度 学习网络。在目标3中，我们将评估用于单纯MR放射治疗的替代CT方法 计划。仅使用MR的方法将与标准的基于CT的大脑、头部治疗模拟进行比较 颈部、胸部、腹部和骨盆以及深度学习网络将针对区域进行优化和评估- 具体的RT规划和仿真。此外，还将研究迁移学习方法，以将SCT扩展到 一台0.35T磁共振直线加速器显示呼吸运动，解决了替代CT生成。

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.