# Simultaneous Multinuclear Magnetic Resonance Fingerprinting for Data Fusion of Quantitative Structural and Metabolic Imaging

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2021 · $634,074

## Abstract

Project Summary
In this project we want to develop a new non-invasive imaging technique that will provide multi-parametric 
metabolic maps of the living brain at an unprecedented resolution. The key to this new technology is the novel
combination of three state-of-the-art imaging concepts: (A) new hardware that enables the simultaneous 
measurement of multinuclear magnetic resonance (MR) signals at different frequencies; (B) the flexibility and robustness
of Plug-and-Play (PnP) MR Fingerprinting (MRF); and (C) a data fusion process driven by a cross-modality model
based on statistical learning. For brevity, we will call this fused simultaneous multinuclear PnP-MRF technique
MNF (Multi-Nuclear Fusion). The idea behind MNF is to rapidly capture two different kinds of quantitative 
information throughout the whole brain in one single scan: (1) structural information from proton (1H) MRF such as
T1, T2, and proton density (PD) (tissue-scale morphology); and (2) metabolic information related to ion 
homeostasis from sodium (23Na) MRF, such as intracellular sodium concentration, and intracellular, extracellular and
cerebrospinal fluid (CSF) volume fractions (cellular-scale function). Because PnP-MRF can quantify multiple 
tissue properties free of experimental bias, it enables us to employ statistical learning to discover a subject-specific
cross-modality model that integrates all voxelwise inter-relationships between the multi-parametric 1H PnP-MRF
(acquired at high resolution, 0.75-1 mm) and 23Na PnP-MRF (acquired at low resolution, 3-5 mm) maps. These
subject-specific relations can subsequently be used to sharpen the 23Na metabolic maps to match the resolution
of the 1H structural maps. The high-resolution 23Na maps will enable the assessment of metabolic processes in
vivo and bridge the gap in resolution that has held back our ability to study metabolism in the living human brain,
which is crucial for our understanding of the brain itself and the afflictions that affect it. This proof-of-concept
implementation will be developed at 7 T, but it is expected to be adaptable to clinical 3 T MR scanners. The
specific aims are: (1) Data acquisition, (1.a) multi-channel 1H/23Na RF array, (1.b) simultaneous multinuclear
3D MRF sequence; (2) Data processing, (2.a) PnP-MRF reconstruction for both 1H data (fingerprint matching
to generate structural maps) and 23Na data (tissue 4-compartment model and simulation of spin 3/2 dynamics
to generate metabolic maps), (2.b) cross-modality model using statistical learning, and data fusion algorithm to
generate high-resolution metabolic maps; (3) Method validation, (3.a) accuracy and precision, (3.b) repeatability
and reproducibility.(3) Exploratory aim: Test MNF on patients with chronic steno-occlusive disease, with 
recurrent transient ischemic attacks (TIA)/minor stroke, presenting regional brain ischemia, at 3 time points (baseline,
8-month and 16-month follow-ups), and comparison with healthy controls.

## Key facts

- **NIH application ID:** 10135060
- **Project number:** 5R01EB026456-04
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Guillaume MADELIN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $634,074
- **Award type:** 5
- **Project period:** 2018-07-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10135060

## Citation

> US National Institutes of Health, RePORTER application 10135060, Simultaneous Multinuclear Magnetic Resonance Fingerprinting for Data Fusion of Quantitative Structural and Metabolic Imaging (5R01EB026456-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10135060. Licensed CC0.

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