# Fast High-Resolution Microstructure Diffusion MRI Exploiting Data Redundancy

> **NIH NIH K99** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $111,119

## Abstract

Project Summary/Abstract
Diffusion magnetic resonance imaging (dMRI) is indispensable in everyday clinical neuroimaging due to its
capacity for noninvasive whole-brain imaging. It has a significant impact on diagnosing conditions affecting
millions of Americans. Furthermore, advanced dMRI holds promise for mapping microstructural tissue features,
revealing hidden damage in white matter, guiding neurosurgery, and studying complex brain structures.
However, lengthy acquisition times prevent advanced diffusion encodings and high-resolution imaging (≤1mm³)
from reaching clinical applications. To bridge this gap, I propose a novel approach to accelerate advanced dMRI
while maintaining image quality and microstructure sensitivity. This innovative method aims to achieve up to
five-fold speed improvement by efficiently exploiting data redundancy. Structural MRI has only focused on spatial
undersampling data acceleration techniques but dMRI datasets are intrinsically of higher dimensionality due to
the multiple volumes of diffusion encodings that are acquired. My goal is to pioneer a novel imaging approach
called zero-shell imaging (ZSI) that uses tissue biophysics to speed up dMRI data acquisition and improve image
resolution. This technique encompasses joint undersampling in both spatial (k-space) and diffusion weighting
(q-space) domains, facilitating direct reconstruction of diffusion contrasts with fewer spatial samples per diffusion
encoding. Accounting for patient motion between samples and merging sequential images, I aim to optimize the
allocation of scan time, to enable higher resolution and to increase the number of diffusion encodings without
lengthening the overall scan time. Preliminary findings have demonstrated that ZSI can successfully
undersample the diffusion acquisition space, separating diffusion weightings and directions. In Aim 1, I will
optimize a method that reconstructs dMRI signal’s rotational invariants from undersampled q-space protocols.
For Aim 2, I will develop a dMRI sequence that performs joint k-q-space undersampling and a motion-robust
reconstruction algorithm. Both aims will include validation on healthy subjects and reproducibility assessment.
In Aim 3, I will perform an evaluation of the clinical utility for tracking disease progression in multiple sclerosis
(MS) patients and for detecting MS brainstem lesions. Overall, this research will develop an innovative imaging
method that has the potential to transform MRI diagnosis. During the K99 phase of the award, I will benefit from
the mentorship of Profs. Novikov, Fieremans, and Feng at New York University Grossman School of Medicine,
by obtaining additional training in pulse sequence design and microstructure-informed image reconstruction. My
proposed training plan will equip me with the necessary research and professional skills to start an independent
career in the R00 phase, in which I will develop innovative solutions that harmoniously optimize image
generation, d...

## Key facts

- **NIH application ID:** 10948991
- **Project number:** 1K99EB036080-01
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Santiago Coelho
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $111,119
- **Award type:** 1
- **Project period:** 2024-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10948991

## Citation

> US National Institutes of Health, RePORTER application 10948991, Fast High-Resolution Microstructure Diffusion MRI Exploiting Data Redundancy (1K99EB036080-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10948991. Licensed CC0.

---

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