# Development of Multi-Compartment MR-Fingerprinting for Subvoxel Estimation of Quantitative Tissue Biomarkers

> **NIH NIH R21** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2020 · $252,175

## Abstract

Project Summary
Magnetic resonance fingerprinting (MRF) has been proposed as a technique to quantify tissue parameters, such
as T1 and T2 relaxation times, which are biomarkers for various pathologies. One assumption of MRF is that the
signal in each voxel is generated by exactly one set of tissue parameters. Due to MRI resolution at the range of
millimeter in combination with the cellular structure of biological tissue, each voxel consists of multiple tissue
compartments. An over-simplified single compartment model results in apparent relaxation times that are
influenced by the relaxation times and the fractional proton densities of all contributing compartments. This can
lead to a misinterpretation of signal changes. For example, in diseases that causes demyelination in white matter
(Multiple Sclerosis, Dementia), a reduction of the myelin water fraction would result in a misleading change of
the apparent relaxation time of the voxel.
We propose a multi-compartment MRF method that allows to identify multiple tissue contributions within a voxel,
including the fractional proton density (PD) of different compartments. Our machine learning based approach
automatically identifies the number of compartments within each voxel that can be identified with the available
SNR in that voxel. We will correct for partial-volume effects at the borders of two types of tissues, as well as
analyze tissue microstructure. For the second case our learned model will also include chemical exchange
between compartments.
After an initial validation phase using numerical simulations, we will first perform MRF scans of dedicated 3D
printed phantoms with multiple compartments. Our quality criterion is successful estimation of all simulated tissue
compartments for all voxels with a relative error of less than 5% to the ground truth. We will then perform in-vivo
MRF measurements of healthy volunteers (n=5). We will generate synthetic FLAIR and MP-RAGE contrasts
from parameter maps estimated with conventional and the proposed multi-compartment MRF technique. We will
compare them with currently used clinical contrasts acquired using established pulse sequences and validate
the performance of our approach by measuring the cortical thickness. Further, we will validate the performance
for microstructure composition in white matter. Our hypothesis is that it will be possible separate the
compartments for myelin, intra- and extra-cellular water and compare the results to ex-vivo data found in
literature.
In summary, the methods developed in this R21 proposal will provide a novel technique to accurately and
reproducibly identify biomarkers beyond the resolution of a voxel. It will allow to identify changes in tissue
composition and fractional proton density at the microstructure level.

## Key facts

- **NIH application ID:** 10016296
- **Project number:** 5R21EB027241-02
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Jakob Asslaender
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $252,175
- **Award type:** 5
- **Project period:** 2019-09-11 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10016296

## Citation

> US National Institutes of Health, RePORTER application 10016296, Development of Multi-Compartment MR-Fingerprinting for Subvoxel Estimation of Quantitative Tissue Biomarkers (5R21EB027241-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10016296. Licensed CC0.

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