# Development of Anatomical Patient Models to Facilitate MR-only Treatment Planning

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2020 · $285,462

## Abstract

Accurate delineation of targets and organs at risk for radiation therapy planning (RTP) remains a challenge
due to the lack of soft tissue contrast in computed tomography (CT), the standard of care imaging for RTP.
Radiation Oncology has addressed this limitation by registering magnetic resonance images (MRI) to CT
datasets to take advantage of the superior soft tissue contrast afforded by MRI. MRI brings considerable value
to RTP by improving delineation accuracy which, in turn, has enabled dose escalation to improve local control
while maintaining or reducing normal tissue toxicities. However, the current integration of MRI as an adjunct to
CT has significant drawbacks as it requires image registration and contour transfer between datasets. This
process introduces systematic geometric uncertainties that persist throughout treatment and may compromise
tumor control. Thus, we propose to translate MR-only RTP into clinical use, with the ultimate goal of improving
patient outcomes accomplished via improved treatment plan design. MR-only RTP will eliminate redundant CT
scans (reducing dose, patient time, and costs), streamline clinical efficiency, entirely circumvent registration
uncertainties, and fully exploit the benefits of MRI for high-precision RTP. Yet, MRI is not routinely used alone
for RTP, largely due to its known spatial distortions, lack of electron density, and inability to segment the bone
needed for online image guidance and electron density mapping for dose calculation.
 The central hypothesis is that the innovative technologies that our multi-disciplinary academic/industrial
(Henry Ford Health System/Philips Healthcare) collaboration develop will yield geometrically accurate patient
models built from MRI data across several platforms/field strengths with CT-equivalent densities that can be
used in confidence throughout the entire RTP workflow. In Aim 1, we will perform geometric distortion
corrections, determine distortion variability with changing anatomy, benchmark the results in a novel modular
phantom, and develop an image processing toolkit. In Aim 2, we will fully automate MR image segmentation in
the brain and male/female pelvis to yield accurate synthetic CT patient models derived from novel MRI
sequences, including provisions for metal implants, and benchmark the results in phantom. In Aim 3, we will
conduct end-to-end testing to characterize the uncertainties in the MR-only RTP workflow. We will perform a
virtual clinical trial of MR-only RTP for brain and male/female pelvis and compare to the standard of care. Final
translation will include developing physician-physicist practice guidelines, end-user validation of all translational
steps, and dissemination of image processing tools into the Radiation Oncology community. This research will
systematically address the major challenges limiting MR-only RTP and lay the groundwork for multi-institutional
clinical trials across MRI platforms. It will support future work related...

## Key facts

- **NIH application ID:** 10228842
- **Project number:** 7R01CA204189-05
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Carri Kaye Glide-Hurst
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $285,462
- **Award type:** 7
- **Project period:** 2016-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10228842

## Citation

> US National Institutes of Health, RePORTER application 10228842, Development of Anatomical Patient Models to Facilitate MR-only Treatment Planning (7R01CA204189-05). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10228842. Licensed CC0.

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