# Optimizing MRI for Radiation Therapy Treatment Planning

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $344,890

## Abstract

Optimal integration of MRI in Radiation Oncology is hindered by the lack of methods that harvest the
significant prior knowledge available to sample the anatomy, biological status, and physiologic motions of
individual patients. While some generic image acquisition methods take advantage of non-specific low rank
structure of human MR signals to achieve some modest acceleration, the wealth of specific prior knowledge,
from both the population of similar patients as well as the specific patient, has yet to be effectively tapped to
guide optimal treatment planning, positioning, and monitoring.
We hypothesize that biological, morphological, and motion models of the patient can be accurately derived
from a limited number of samples aided by prior knowledge. These advances will allow us to reduce scan times
dramatically (to less than 10% of conventional scanning) for morphological imaging, support efficient
biological imaging for high order diffusion modeling and create hierarchical motion-frozen image volumes of
abdominal patients that simultaneously provide breathing, GI contraction, and potentially cardiac motion
models with probability density functions that can be used to estimate the impact of intrafraction motion on
treatments and eventually select local navigators for real-time monitoring of specific regions that are most
sensitive to motion-related impacts on delivered doses to targets or organs at risk. We will investigate this
hypothesis by developing a prior knowledge-based compressed sensing method to reconstruct densely sampled
DW attenuation curves from sparsely sampled ones; performing principal component analysis of previously
scanned FLAIR, contrast-enhanced T1-weighted and Diffusion-Weighted image volumes to support sparse
sampling in k-space for anatomic imaging and in b-values for diffusion imaging; investigating potential gains
in acceleration of imaging by combining a patient-specific prior with population-derived principal components
of structure and diffusion; modeling breathing and peristaltic motion. Finally, we will develop and implement
scanning sequences based on the modeled methods for subsampling b-values and anatomy. By these methods,
we expect to provide efficient anatomic and high order diffusion imaging, as well as introduce means to
automatically extract hierarchical motion models of the patient for use in treatment planning and future
support of treatment monitoring.
Relevance to PAR 18-484 (for the NCI): This investigation seeks to improve both the efficiency as well as
the efficacy of precision radiation therapy for patients with GBMs, other intracranial targets as well as
intrahepatic tumors. As Radiation therapy is part of the standard armamentarium of care options for these
patients, this research falls within the purview of the NCI.

## Key facts

- **NIH application ID:** 9979862
- **Project number:** 5R01EB016079-07
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** JAMES M BALTER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $344,890
- **Award type:** 5
- **Project period:** 2013-04-01 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9979862

## Citation

> US National Institutes of Health, RePORTER application 9979862, Optimizing MRI for Radiation Therapy Treatment Planning (5R01EB016079-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9979862. Licensed CC0.

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