# Optimized High-Resolution Fast Magnetic Resonance Fingerprinting with Cloud-Based Reconstruction

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $621,617

## Abstract

Optimized High-Resolution Fast Magnetic Resonance Fingerprinting with Cloud-
Based Reconstruction
Abstract
Magnetic resonance imaging (MRI), despite its wide utility, is inherently limited due to its inability to measure
tissue properties quantitatively, which is critical for objective and scanner-independent diagnosis and treatment
monitoring. MR Fingerprinting (MRF) is a relatively new quantitative MRI framework for simultaneous
quantification of multiple tissue properties. While MRF outperforms most conventional methods in quantitative
imaging, existing MRF techniques are still handicapped by limited spatial resolution and coverage, long
acquisition times, suboptimal acquisition parameters, long data reconstruction times, and complicated post-
processing workflows, hindering large-scale clinical validation and translation.
In this project, we will leverage the expertise of our team in MRF, machine learning, and pulse sequence
optimization to develop and optimize a rapid and robust quantitative MR technique, applicable to high-resolution
volumetric brain imaging. Our team has recently developed a new B1-insensitive MRF method using low flip
angles and multiple magnetization preparations for improved accuracy and precision in tissue quantification
compared with existing MRF methods. We will first develop and optimize this new MRF method for 3D high-
resolution brain imaging, using our newly developed pulse sequence design framework. Novel fat navigator will
be incorporated to improve motion robustness (Aim 1). We will leverage state-of-the-art deep learning techniques
to accelerate both acquisition and post-processing (Aim 2). Finally, a complete MRF post-processing pipeline
empowered by GPU cloud computing will be developed to significantly simplify the post-processing workflow
and facilitate efficient clinical translation and validation of the proposed methods for patients with neurological
diseases (Aim 3).

## Key facts

- **NIH application ID:** 10980955
- **Project number:** 1R01NS134849-01A1
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Yong Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $621,617
- **Award type:** 1
- **Project period:** 2024-09-12 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10980955

## Citation

> US National Institutes of Health, RePORTER application 10980955, Optimized High-Resolution Fast Magnetic Resonance Fingerprinting with Cloud-Based Reconstruction (1R01NS134849-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10980955. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*