# Optimized MR Fingerprinting for Rapid Volumetric Quantitative Neuroimaging

> **NIH NIH R00** · UNIVERSITY OF TEXAS AT AUSTIN · 2022 · $248,999

## Abstract

PROJECT SUMMARY/ABSTRACT
 MRI scans are primarily performed and evaluated in a qualitative way using contrast-weighted images (e.g.,
with T1, T2 or proton-density weighting). This image weighting is a nonlinear function of one or more of these
intrinsic MR tissue parameters as modulated by external scanner settings and imperfections. In quantitative
mapping of MR tissue parameters, we attempt to unravel this complex combination to provide a direct
characterization of the tissue parameter in absolute units. This has potential to improve direct comparisons of
scans across different institutions and/or scanners, and also facilitates the understanding of disease progression
and treatment for a single patient across time. Although the potential of quantitative MRI has long been
recognized, its use has been limited by lengthy acquisition times. Magnetic resonance fingerprinting (MRF) is a
recent breakthrough in quantitative MRI that enables simultaneous measurements of multiple tissue parameters
in a single experiment, dramatically shortening acquisition time to ~15 sec per imaging slice and providing
intrinsically registered maps. However, this can still result in unacceptably lengthy acquisitions for high-resolution,
volumetric quantitative imaging. For example, MRF can take up to 20 min for a volumetric whole-brain acquisition
with a spatial resolution of 1.2×1.2×5 mm3, a resolution which, itself, falls short of that needed for structural
neuroimaging analysis. The major deficiency is due to the sub-optimal data acquisition and image reconstruction
schemes currently employed.
 In this application, we will optimize the data acquisition and image reconstruction for MRF by a rigorous
statistical signal processing framework, with an overall goal of improving the accuracy and speed of for volumetric
neuroimaging. In particular, we will exploit the tremendous flexibility/freedom inherent to volumetric acquisition
and image reconstruction to improve accuracy and efficiency. Specifically, we will address the image
reconstruction problem with a principled statistical reconstruction approach that incorporates (1) a data model
for multi-channel acquisitions, (2) a low-rank tensor image model for volumetric time-series images, and (3) a
statistical noise model. We will characterize the reconstruction performance (e.g., error bars) by calculating the
constrained Cramer-Rao bounds (CRB) under low-rank tensor models. We address the data acquisition
problem, by utilizing the constrained CRB as metrics to optimize MRF data acquisition parameters (e.g., flip
angle and repletion time schedule) and k-space trajectories (e.g., stack-of-spiral trajectories) for improved SNR
efficiency. Together, we expect that the proposed technique produces 2x more accurate MR tissue
parameter maps, enabling a desirable resolution (e.g., isotropic 0.8 mm3) and a whole-brain coverage in
a short acquisition time (e.g., 3 minutes). Finally, we will systematically validate the performance o...

## Key facts

- **NIH application ID:** 10450170
- **Project number:** 5R00EB027181-05
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Bo Zhao
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $248,999
- **Award type:** 5
- **Project period:** 2020-09-21 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10450170

## Citation

> US National Institutes of Health, RePORTER application 10450170, Optimized MR Fingerprinting for Rapid Volumetric Quantitative Neuroimaging (5R00EB027181-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10450170. Licensed CC0.

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