Multi‐band‐ and in‐plane‐accelerated diffusion MRI enabled by model‐based deep learning in q‐space and its extension to learning in the spherical harmonic domain

Merry Mani; Baolian Yang; Girish Bathla; Vincent Magnotta; Mathews Jacob

doi:10.1002/mrm.29095

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Multi‐band‐ and in‐plane‐accelerated diffusion MRI enabled by model‐based deep learning in q‐space and its extension to learning in the spherical harmonic domain

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Multi‐band‐ and in‐plane‐accelerated diffusion MRI enabled by model‐based deep learning in q‐space and its extension to learning in the spherical harmonic domain

Merry Mani, Baolian Yang, Girish Bathla, Vincent Magnotta and Mathews Jacob

Magnetic resonance in medicine, Vol.87(4), pp.1799-1815

11/26/2021

DOI: 10.1002/mrm.29095

PMCID: PMC8855531

PMID: 34825729

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https://www.ncbi.nlm.nih.gov/pmc/articles/8855531View

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Abstract

Purpose To propose a new method for the recovery of combined in-plane- and multi-band (MB)-accelerated diffusion MRI data. Methods Combining MB acceleration with in-plane acceleration is crucial to improve the time efficiency of high (angular and spatial) resolution diffusion scans. However, as the MB factor and in-plane acceleration increase, the reconstruction becomes challenging due to the heavy aliasing. The new reconstruction utilizes an additional q-space prior to constrain the recovery, which is derived from the previously proposed qModeL framework. Specifically, the qModeL prior provides a pre-learned representation of the diffusion signal space to which the measured data belongs. We show that the pre-learned q-space prior along with a model-based iterative reconstruction that accommodate multi-band unaliasing, can efficiently reconstruct the in-plane- and MB-accelerated data. The power of joint reconstruction is maximally utilized by using an incoherent under-sampling pattern in the k-q domain. We tested the proposed method on single- and multi-shell data, with high/low angular resolution, high/low spatial resolution, healthy/abnormal tissues, and 3T/7T field strengths. Furthermore, the learning is extended to the spherical harmonic basis, to provide a rotational invariant learning framework. Results The qModeL joint reconstruction is shown to simultaneously unalias and jointly recover DWIs with reasonable accuracy in all the cases studied. The reconstruction error from 18-fold accelerated multi-shell datasets was <3%. The microstructural maps derived from the accelerated acquisitions also exhibit reasonable accuracy for both healthy and abnormal tissues. The deep learning (DL)-enabled reconstructions are comparable to those derived using traditional methods. Conclusion qModeL enables the joint recovery of combined in-plane- and MB-accelerated dMRI utilizing DL.

Details

Title: Subtitle: Multi‐band‐ and in‐plane‐accelerated diffusion MRI enabled by model‐based deep learning in q‐space and its extension to learning in the spherical harmonic domain
Creators: Merry Mani - University of Iowa
Baolian Yang - GE Healthcare Waukesha Wisconsin USA
Girish Bathla - University of Iowa
Vincent Magnotta - University of Iowa
Mathews Jacob - University of Iowa
Resource Type: Journal article
Publication Details: Magnetic resonance in medicine, Vol.87(4), pp.1799-1815
DOI: 10.1002/mrm.29095
PMID: 34825729
PMCID: PMC8855531
NLM abbreviation: Magn Reson Med
ISSN: 0740-3194
eISSN: 1522-2594
Grant note: DOI: 10.13039/100005440, name: Center for Scientific Review, award: 1R01EB019961‐01A1, 1S10OD025025‐01, 1S10RR028821‐01, 5 R01 MH111578, R01 EB022019
Language: English
Date published: 11/26/2021
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Psychiatry; Iowa Neuroscience Institute; Radiation Oncology
Record Identifier: 9984198054302771

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