# Quantitative MRI of Multiple Sclerosis - Resubmission - 1

> **NIH NIH F31** · UNIVERSITY OF CHICAGO · 2020 · $45,520

## Abstract

Project Summary
 In this project, we propose a novel T1 and T2 quantification method that generates quantitative T1 or T2
maps from weighted MR images. Magnetic resonance imaging (MRI) is commonly used as a tool to diagnose
Multiple Sclerosis (MS) and track lesional changes over time. Because MRI has various contrasts that display
different information about the underlying tissue microstructure and physiology, it can potentially be used as a
tool to predict MS disease progression and even disability. However, there is no known measure derived from
MR images of MS that correlates well with clinical disability as described by the Expanded Disability Status Score
(EDSS). Previous efforts to correlate MRI features and EDSS have included calculating total lesion load on T1-
and T2-weighted images, measuring the variations in the magnetic transfer ration of normal-appearing brain
tissues, and calculating cerebral atrophy, each with a varying level of success. Yet, there has been little study of
the evolution of relaxation times of the lesions over time and how it relates to disability. Because changes in the
T1 (spin-lattice) and T2 (spin-spin) relaxation times of a tissue can reflect pathological changes in that tissue
over time, quantitative T1 and T2 maps derived from MR images may be more indicative of microscopic changes
that manifest as disability in MS patients.
 The specific aims of this proposal are: (1) develop and validate novel T1 and T2 quantification method on
spin-echo MR images, (2) extend the novel quantification method to common MS imaging sequences, and (3)
apply the novel quantification method to MS datasets to predict EDSS using machine learning. Aim 1 will involve
the validation of the quantification pipeline on both T1- and T2-weighted spin-echo MR images in vivo, resulting
in a range of acceptable parameters for the novel quantification method. Aim 2 will extend the quantification
pipeline to include commonly used and more complicated MS imaging sequences, again resulting in a range of
acceptable parameters for the quantification method. Aim 3 will use the quantification pipeline to compare
machine learning algorithms with and without quantification to determine the added value of quantification in the
imaging of MS. Additionally, Aim 3 will result in a predictive machine learning model utilizing multiple imaging
contrasts for the prediction of disability in MS. These results will provide a more thorough understanding of the
role of MR quantification in the evaluation of neurological diseases, such as MS, and will offer a scientific
foundation to extend the use of MR quantification as a potential imaging biomarker for other diseases and
pathologies.

## Key facts

- **NIH application ID:** 10154293
- **Project number:** 1F31NS118930-01A1
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Adam James Hasse
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $45,520
- **Award type:** 1
- **Project period:** 2020-09-22 → 2022-09-21

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10154293

## Citation

> US National Institutes of Health, RePORTER application 10154293, Quantitative MRI of Multiple Sclerosis - Resubmission - 1 (1F31NS118930-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10154293. Licensed CC0.

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