Improved Techniques for Substitute CT Generation from MRI datasets

该提案将改进替代 CT 图像，用于 PET/MR 和仅 MR 放射治疗 规划。鉴于 MR 相对于 CT 的软组织对比度大大提高，这有助于解释 PET 对于 PET/MR 和放射治疗计划的靶区勾画，剩下的一个限制是当前 能够仅从 MR 数据获得足够准确的替代 CT 图像。不幸的是，MRI 解析骨骼的能力有限，并且大多数 MR 采集无法区分空气和骨骼 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 不断发展的机器学习领域，开发新方法来从快速 MR 创建替代 CT 图像 可用于 PET/MR 和放射治疗计划工作流程的采集。在目标 1 中，我们将学习 快速 MR 采集与深度学习方法一起用于头部和骨盆的 SCT 生成 使用 3T PET/MR 图像与 PET/CT 成像相匹配来创建深度学习训练和评估 数据集。将研究并调整不同的深度学习网络和 MR 输入以确定最佳的 PET 重建性能。在目标 2 中，我们将研究快速但具有运动弹性的方法来实现整体- 用于后续基于深度学习的替代 CT 生成的身体 MR 成像。在探索性子目标中，我们 还建议研究仅利用 PET 数据的 sCT 生成方法。目标 2 中获取的数据 将用于创建全面的全身运动弹性数据集，用于深度训练和评估 学习网络。在目标 3 中，我们将评估仅 MR 放射治疗的替代 CT 方法 规划。仅 MR 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规划和模拟。此外，还将研究迁移学习方法，将 sCT 扩展到 0.35T MR-Linac 用于演示呼吸运动解析替代 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.