# Multi-Task MR Simulation for Abdominal Radiation Treatment Planning

> **NIH NIH R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2023 · $681,141

## Abstract

The accuracy of radiation treatment planning (RTP) heavily influences the effectiveness of external beam
radiotherapy (EBRT). Individualized RTP begins with a “simulation”, in which the patient in a treatment position
is commonly scanned using computed tomography (CT) to define the treatment target and organs at risk (OARs).
When soft-tissue contrast is inadequate to support accurate target and OAR delineation in CT based RTP,
conservatively large treatment margins are used to avoid a geometric miss. The crude treatment prevents
delivering sufficient radiation dose to the tumor without exceeding the tolerance of surrounding normal tissues.
Magnetic resonance (MR) can be used as a simulation platform complementary to CT for improved soft-tissue
conspicuity. Yet, such a complicated, costly and tedious multi-modal RTP workflow along with unavoidable
systematic MR-CT co-registration errors has limited its applications in EBRT, especially at the abdominal site
whereby anatomies are highly mobile. Over the past few years, there is a keen interest in the integration of MR
alone into RTP and even the therapy workflow (i.e. MR-guided radiotherapy, MRgRT). The abdomen poses
critical challenges to MR simulation. Current MR imaging sequences are suboptimal to produce motion-free
images and resolve respiratory motion. MR data processing for abdominal RTP is underdeveloped. Contouring
of target and OARs typically relies on manual, tedious procedures that are time-consuming and variation-prone.
In this proposal, we will substantially improve the MR acquisition and automated multi-organ segmentation, so
the potential of MR as a simulation modality can be fully unleashed for abdominal EBRT. Three specific aims
will be completed. In Aim 1, we will develop and validate a standalone multi-task MR (MT-MR) sequence
dedicated to abdominal MR simulation. In Aim 2, we will develop an MT-MR simulation based multi-organ auto-
segmentation tool. In Aim 3, we will optimize a deep learning-based dose prediction model and assess the
effectiveness of the MT-MR based RTP workflow in adaptive stereotactic body radiotherapy planning of
pancreatic cancer patients. Successful completion of the project will significantly promote the clinical adoption of
MR simulation for abdominal RTP, which will improve treatment precision and outcomes. Moreover, the
developed techniques will open the door to future studies aiming at optimizations in both cancer diagnosis and
radiotherapy.

## Key facts

- **NIH application ID:** 10606517
- **Project number:** 5R01EB029088-04
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Zhaoyang Fan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $681,141
- **Award type:** 5
- **Project period:** 2021-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10606517

## Citation

> US National Institutes of Health, RePORTER application 10606517, Multi-Task MR Simulation for Abdominal Radiation Treatment Planning (5R01EB029088-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10606517. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*