Improved Techniques for Substitute CT Generation from MRI datasets

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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.