# Reporting Quantitative Microstructural MRI Metrics to Stage and Monitor Multiple Sclerosis

> **NIH NIH R43** · MICROSTRUCTURE IMAGING, INC. · 2024 · $353,942

## Abstract

PROJECT SUMMARY
At least 400,000 people in the United States are affected by Multiple sclerosis (MS), which is the most prevalent
chronic inflammatory disease of the central nervous system (CNS). The current non-invasive MRI gold-standard
of MS characterization is through volumetric biomarkers, primarily characterizing lesion load (number and
volume of lesions), or brain volume atrophy (which is accelerated in MS brains). However, volume loss
associated with underlying neurodegeneration in MS over a 6-12 month period, while abnormal, may still be less
than the precision of the volumetric measurement, such that volume changes in individual MS patients may only
be confidently diagnosed over 18-24 months and based on multiple measurements. The volumetric
measurement also is most valid and precise only when obtained on the same MRI scanner each time, which is
often impractical in the current US healthcare environment.
Our approach is to derive quantitative MRI biomarkers from diffusion MRI (dMRI), which describe properties of
the microscopic environment and has been shown to outperform volumetrics. There has been a global effort to
reparametrize the WM dMRI signal, through the standard model (SMI) of white matter, as the summation of
tissue compartments typically over intra-axonal, extra-axonal and free-water compartments, enabling
disentangling inflammatory and degenerative processes.
Aim 1. Curate database of age- and sex-matched normative reference SMI parameters. In collaboration
with NYU’s Multiple Sclerosis Comprehensive Center (MSCC), we will retrospectively reprocess multi-shell dMRI
data in adult populations for normal (N=544) and MS (N=418) patients to define clinically relevant SMI ranges.
Aim 2. Develop a software platform to automate biomarker analysis. Using data from Aim 1, an FDA-
compliant backend software pipeline performing image processing, statistical comparisons and end-user
reporting will be developed in Python and containerized for cross-platform generalization. The end-product is the
ms-MICSI report to aid clinicians in prognostic decision making and inform pharmaceutical drug trials.
Aim 3. Verification and Validation of the ms-MICSI report will be performed on (A) 15 healthy volunteers, (B)
40 MS patients, 15 of which will return for a follow-up in 9 months to be imaged, and (C) scan-rescan (N=20
age/sex matched healthy volunteers) to evaluate bias and precision of SMI parameters. Cross sectional and
longitudinal changes observed by ms-MICSI will be compared against commercially available volumetrics.

## Key facts

- **NIH application ID:** 10920523
- **Project number:** 1R43NS137871-01
- **Recipient organization:** MICROSTRUCTURE IMAGING, INC.
- **Principal Investigator:** Grigoriy Lemberskiy
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $353,942
- **Award type:** 1
- **Project period:** 2024-09-10 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10920523

## Citation

> US National Institutes of Health, RePORTER application 10920523, Reporting Quantitative Microstructural MRI Metrics to Stage and Monitor Multiple Sclerosis (1R43NS137871-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10920523. Licensed CC0.

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