Improved MUSSELS reconstruction for high‐resolution multi‐shot diffusion weighted imaging

Merry Mani; Hemant Kumar Aggarwal; Vincent Magnotta; Mathews Jacob

doi:10.1002/mrm.28090

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Improved MUSSELS reconstruction for high‐resolution multi‐shot diffusion weighted imaging

Journal article

Peer reviewed

Improved MUSSELS reconstruction for high‐resolution multi‐shot diffusion weighted imaging

Merry Mani, Hemant Kumar Aggarwal, Vincent Magnotta and Mathews Jacob

Magnetic resonance in medicine, Vol.83(6), pp.2253-2263

06/2020

DOI: 10.1002/mrm.28090

PMCID: PMC8045517

PMID: 31789440

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https://www.ncbi.nlm.nih.gov/pmc/articles/8045517View

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Abstract

Purpose MUSSELS is a one‐step iterative reconstruction method for multishot diffusion weighted (msDW) imaging. The current work presents an efficient implementation, termed IRLS MUSSELS, that enables faster reconstruction to enhance its utility for high‐resolution diffusion MRI studies. Methods The recently proposed MUSSELS reconstruction belongs to a new class of parallel imaging‐based methods that recover artifact‐free DWIs from msDW data without needing phase compensation. The reconstruction is achieved via structured low‐rank matrix completion algorithms, which are computationally demanding due to the large size of the Hankel matrices and their associated computations involving singular value decompositions. Because of this, computational demands of the MUSSELS reconstruction scales as the matrix size and the number of shots increases, which hinders its practical utility for high‐resolution applications. In this work, we derive a computationally efficient MUSSELS formulation by modifying the iterative reweighted least squares (IRLS) method that were proposed earlier to solve such problems. Using whole‐brain in vivo data, we show the utility of the IRLS MUSSELS for routine high‐resolution studies with reduced computational burden. Results IRLS MUSSELS provides about five times faster reconstruction for matrix sizes 192 × 192 and 256 × 256 compared to the earlier MUSSELS implementation. The widely employed conjugate symmetry priors can also be incorporated into IRLS MUSSELS to reduce blurring of the partial Fourier acquisitions, without incurring much computational burden. Conclusions The proposed method is observed to be computationally efficient to enable routine high‐resolution studies. The computational complexity matches the traditional msDWI reconstruction methods and provides improved reconstruction results with the additional constraints.

UHF DWI

structured low rank

7T diffusion MRI

diffusion motion compensation

high resolution

Details

Title: Subtitle: Improved MUSSELS reconstruction for high‐resolution multi‐shot diffusion weighted imaging
Creators: Merry Mani - University of Iowa
Hemant Kumar Aggarwal - University of Iowa
Vincent Magnotta - University of Iowa
Mathews Jacob - University of Iowa
Resource Type: Journal article
Publication Details: Magnetic resonance in medicine, Vol.83(6), pp.2253-2263
DOI: 10.1002/mrm.28090
PMID: 31789440
PMCID: PMC8045517
NLM abbreviation: Magn Reson Med
ISSN: 0740-3194
eISSN: 1522-2594
Number of pages: 11
Grant note: MRI instruments (1S10OD025025‐01; 1S10RR028821‐01) NIH (5 R01 EB022019; 5 R01 MH111578; 1R01EB019961‐01A1)
Language: English
Date published: 06/2020
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Psychiatry; Iowa Neuroscience Institute; Radiation Oncology
Record Identifier: 9984070709702771

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