# Improved Techniques for Substitute CT Generation from MRI datasets

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2020 · $458,900

## Abstract

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.

## Key facts

- **NIH application ID:** 9927625
- **Project number:** 5R01EB026708-03
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Alan Blair McMillan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $458,900
- **Award type:** 5
- **Project period:** 2018-08-10 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9927625

## Citation

> US National Institutes of Health, RePORTER application 9927625, Improved Techniques for Substitute CT Generation from MRI datasets (5R01EB026708-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9927625. Licensed CC0.

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