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Fast High-Resolution Microstructure Diffusion MRI Exploiting Data Redundancy

Award Number: R00EB036080
ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/01/2024
PERIOD OF PERFORMANCE END DATE: 07/31/2029

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Fast High-Resolution Microstructure Diffusion MRI Exploiting Data Redundancy - 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 (1mm^3) 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. While conventional MRI acceleration has focused exclusively on spatial undersampling, dMRI datasets are intrinsically higher-dimensional due to the multiple diffusion encoding volumes acquired, opening additional opportunities for acceleration. 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. During the K99 phase of this award, I laid the technical foundations for ZSI. I have demonstrated that ZSI can successfully undersample the diffusion acquisition space, separating diffusion weightings and directions. I have shown that it is possible to reconstruct dMRI signal's rotational invariants, orientation-independent microstructure features, from undersampled q-space datasets. Additionally, joint k-q-space undersampling has been shown feasible for speeding up data acquisition. In the R00 phase, I will perform a more comprehensive validation of my k-q joint image reconstruction together with 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 continue to develop an innovative imaging method that has the potential to transform MRI diagnosis. This project combines, for the first time, the low-rank nature of MRI images (k-space) together with microstructure modeling constraints that effectively factorize and reduce the dimensionality of the diffusion weighting space (q-space). This work is motivated by observations that the combined k-q-space samples are sparse and permit massive undersampling. In the long term, the increased speed enabled by this project may finally enable the translation of microstructure imaging to clinic.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $248,999 )
2026	2026	NEW YORK UNIVERSITY	550 1ST AVE	NEW YORK	NY	10016	NEW YORK	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	3	9/1/2026	EXTENSION WITH OR WITHOUT FUNDS	$248,999
														Subtotal = $248,999

Grand Total All Awards = $248,999

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Fast High-Resolution Microstructure Diffusion MRI Exploiting Data Redundancy

Award Number: R00EB036080

ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 08/01/2024

PERIOD OF PERFORMANCE END DATE: 07/31/2029

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