# MODELING MRI-BASED TISSUE RELAXATION IN THE PRESENCE OF IRON OVERLOAD AND STEATOSIS

> **NIH NIH R21** · UNIVERSITY OF MEMPHIS · 2021 · $298,619

## Abstract

Project Summary
Iron overload, either inherited or acquired through chronic blood transfusions, affects about 16 million Americans.
Steatosis (`fat overload') affects one-third of the US population and is linked with obesity, insulin resistance, and
metabolic syndromes. Co-occurrence of hepatic iron overload and steatosis is a common manifestation of diffuse
liver diseases, chronic hepatopathies, and cancer therapy and can cause iron- and lipo-toxicity leading to
progressive fibrosis, irreversible cirrhosis, and ultimately, organ failure. Magnetic resonance imaging (MRI) is a
clinically important non-invasive tool for assessing hepatic iron overload and steatosis independently. However,
in co-existing conditions, MRI quantification is often inaccurate due to confounding effects of iron and fat on MRI
signal. Multi-spectral signal models accounting for these confounding effects have been proposed for
simultaneous quantification of transverse relaxation rate (R2*), a predictor for iron content, and fat fraction (FF).
However, these models were optimized and validated in only patients with steatosis and failed in different co-
existing hepatic iron and fat overload conditions. The models assume either single or dual R2* for water and fat
protons, and any incorrect assumptions or instabilities in the signal model produce errors in R2* and FF
calculations, leading to misdiagnosis. The assumption to use single or dual R2* depends on the dephasing
effects of in vivo iron deposits on water and fat protons, and these effects, in turn, depend on the size and
distribution of iron and fat deposits on the microscopic scale. Previous simulation and phantom studies
investigating the performances of multi-spectral signal models did not use a realistic tissue model. In simulation
study, the sizes and distribution of iron and fat molecules were not considered, and in phantom studies, the sizes
of iron particles and fat droplets did not match the scales of in vivo iron and fat deposits. Hence, there is a void
in our understanding of how the true microscopic arrangement of iron and fat deposits in vivo will cause
susceptibility-induced inhomogeneities and affect the macroscopic MRI signal relaxation. In this proposed
research, we will perform a rigorous investigation for evaluating the contribution of size and distribution of iron
and fat deposits on MRI signal via simulations, phantom experiments, and in vivo studies to determine an
accurate MRI signal model for simultaneous and accurate assessment of iron overload and steatosis. We will
(a) develop a Monte Carlo–based approach for creating virtual liver models with iron overload, steatosis, or both
and simulating iron-proton interactions; (b) construct realistic phantoms with different particle sizes mimicking in
vivo iron and fat deposits; and (c) validate MRI signal behavior in phantoms and retrospective patients by using
biopsy assessments as a reference standard. This research will aid our understanding and qu...

## Key facts

- **NIH application ID:** 10196572
- **Project number:** 1R21EB031298-01
- **Recipient organization:** UNIVERSITY OF MEMPHIS
- **Principal Investigator:** Aaryani Tipirneni-Sajja
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $298,619
- **Award type:** 1
- **Project period:** 2021-05-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10196572

## Citation

> US National Institutes of Health, RePORTER application 10196572, MODELING MRI-BASED TISSUE RELAXATION IN THE PRESENCE OF IRON OVERLOAD AND STEATOSIS (1R21EB031298-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10196572. Licensed CC0.

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