High-dimensional fast convolutional framework (HICU) for calibrationless MRI.

Tytuł:: High-dimensional fast convolutional framework (HICU) for calibrationless MRI.
Autorzy:: Zhao S; Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.
Potter LC; Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.; Davis Heart & Lung Research Institute, The Ohio State University, Columbus, OH, USA.
Ahmad R; Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.; Davis Heart & Lung Research Institute, The Ohio State University, Columbus, OH, USA.; Biomedical Engineering, The Ohio State University, Columbus, OH, USA.
Źródło:: Magnetic resonance in medicine [Magn Reson Med] 2021 Sep; Vol. 86 (3), pp. 1212-1225. Date of Electronic Publication: 2021 Apr 04.
Typ publikacji:: Journal Article; Research Support, N.I.H., Extramural
Język:: English
Imprint Name(s):: Publication: 1999- : New York, NY : Wiley
Original Publication: San Diego : Academic Press,
MeSH Terms:: Imaging, Three-Dimensional*
Magnetic Resonance Imaging*
Brain/diagnostic imaging ; Diffusion Magnetic Resonance Imaging ; Echo-Planar Imaging
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Grant Information:: R01 HL135489 United States HL NHLBI NIH HHS
Contributed Indexing:: Keywords: blind multi-coil deconvolution; calibrationless MRI; parallel imaging; structured low-rank matrix completion
Entry Date(s):: Date Created: 20210405 Date Completed: 20210617 Latest Revision: 20231111
Update Code:: 20240104
PubMed Central ID:: PMC8184615
DOI:: 10.1002/mrm.28721
PMID:: 33817823
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Purpose: To present a computational procedure for accelerated, calibrationless magnetic resonance image (Cl-MRI) reconstruction that is fast, memory efficient, and scales to high-dimensional imaging.
Theory and Methods: Cl-MRI methods can enable high acceleration rates and flexible sampling patterns, but their clinical application is limited by computational complexity and large memory footprint. The proposed computational procedure, HIgh-dimensional fast convolutional framework (HICU), provides fast, memory-efficient recovery of unsampled k-space points. For demonstration, HICU is applied to 6 2D T2-weighted brain, 7 2D cardiac cine, 5 3D knee, and 1 multi-shot diffusion weighted imaging (MSDWI) datasets.
Results: The 2D imaging results show that HICU can offer 1-2 orders of magnitude computation speedup compared to other Cl-MRI methods without sacrificing imaging quality. The 2D cine and 3D imaging results show that the computational acceleration techniques included in HICU yield computing time on par with SENSE-based compressed sensing methods with up to 3 dB improvement in signal-to-error ratio and better perceptual quality. The MSDWI results demonstrate the feasibility of HICU for a challenging multi-shot echo-planar imaging application.
Conclusions: The presented method, HICU, offers efficient computation and scalability as well as extendibility to a wide variety of MRI applications.
(Copyright © 2021 International Society for Magnetic Resonance in Medicine.)

Erratum in: Magn Reson Med. 2022 Jun;87(6):3027. (PMID: 35338524)

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