# Multicomponent Modeling of High-Dimensional Multiparametric MRI Data

> **NIH NIH R56** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2023 · $836,443

## Abstract

PROJECT SUMMARY
 Multicomponent Modeling of High-Dimensional Multiparametric MRI Data
MRI generates images with millimeter-scale spatial resolution, while many important biological features occur
at much smaller (microscopic) scales. Over the past several decades, MRI practitioners have used the
information derived from biophysical parameters (like relaxation and diffusion) to indirectly probe microscopic
tissue compartments using millimeter-scale data. While these approaches have been somewhat successful, an
innovative new paradigm has emerged in recent years that leverages multiparametric MRI data (e.g., using
relaxation and diffusion jointly in a higher-dimensional experiment) to probe tissue microstructure with an
unprecedented level of detail. Although such multicomponent multiparametric methods can be quite powerful,
substantial improvements in image acquisition and analysis methods and improvements in accessibility are
needed for these approaches to be used routinely for practical applications by the broader community. The
proposed project involves the development of novel analysis methods to identify and separate multiple
microstructural tissue compartments from MRI data using advanced constrained estimation techniques, the
development of a novel end-to-end image preprocessing pipeline (including steps like registration, distortion
correction, denoising, etc.) that is especially designed for multiparametric acquisitions, the development of
novel tools to evaluate estimation quality and optimize multiparametric acquisition protocols, and the
application of this approach to ex vivo mouse brains and spinal cords to provide new insights into sex
differences and the role of oxidative stress in a mouse model of multiple sclerosis. Further, the new methods
we develop will be integrated into the open-source BrainSuite Diffusion Pipeline software package, which will,
for the first time, provide the broader imaging community with easy access to these powerful approaches.

## Key facts

- **NIH application ID:** 10861533
- **Project number:** 1R56EB034349-01
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Justin Pritam Haldar
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $836,443
- **Award type:** 1
- **Project period:** 2023-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10861533

## Citation

> US National Institutes of Health, RePORTER application 10861533, Multicomponent Modeling of High-Dimensional Multiparametric MRI Data (1R56EB034349-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10861533. Licensed CC0.

---

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