# Computational imaging biomarkers of multiple sclerosis

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $371,019

## Abstract

Abstract
Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system that causes significant
cognitive and motor deficits and affects nearly half a million Americans and 2.5 million individuals worldwide. In
vivo MRI can detect the disease’s hallmark white matter lesions and their changes over time with a significantly
higher sensitivity than clinical assessment of disease activity. Furthermore, numerous studies have shown that
the atrophy accrual in various brain structures, assessed from serial MRI, is faster in patients with MS than in
healthy controls, and correlates with measures of disability. Therefore, the ability to reliably and efficiently
characterize the morphometry of white matter lesions, various neuroanatomical structures, and their changes
over time directly from in vivo MRI would be of great potential value for diagnosing disease, tracking
progression, and evaluating treatment.
While many automatic tools for segmenting white matter lesions from MR scans of MS patients have been
developed, these are typically tuned for specific research protocols only, and do not address the problem of
characterizing brain atrophy patterns in MS patients, where the presence of lesions is known to interfere with
atrophy estimation. Furthermore, computational neuroimaging efforts in MS have been focused almost
exclusively on demonstrating statistical associations on population levels, rather than on prediction models that
combine all sources of information simultaneously to compute the most sensitive biomarker in individual
patients.
In order to address these limitations, this project aims to (1) develop and validate automated tools for scanner-
adaptive segmentation of white matter lesions within their neuroanatomical context; (2) develop and deploy
spatially regularized models for predicting disability at the level of the individual patient; and (3) generalize,
validate, and apply the proposed segmentation and prediction tools in longitudinal settings. The successful
completion of this project will result in a set of computational imaging biomarkers in MS that correlate better
with clinical observation than currently available methods; publicly available software tools for robustly
segmenting longitudinal scans of MS patients across a wide range of imaging hardware and protocols; and a
more detailed characterization of the morphological and temporal dynamics underlying disease progression
and accumulation of disability in MS.

## Key facts

- **NIH application ID:** 10005502
- **Project number:** 5R01NS112161-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Koen Van Leemput
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $371,019
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10005502

## Citation

> US National Institutes of Health, RePORTER application 10005502, Computational imaging biomarkers of multiple sclerosis (5R01NS112161-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10005502. Licensed CC0.

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