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Compressed SVD-based L + S model to reconstruct undersampled dynamic MRI data using parallel architecture.

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Title: Compressed SVD-based L + S model to reconstruct undersampled dynamic MRI data using parallel architecture.
Authors: Shafique M; Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan. engr.shafique@upr.edu.pk.; Department of Electrical Engineering, University of Poonch Rawalakot, Rawalakot, AJ&K, Pakistan. engr.shafique@upr.edu.pk., Qazi SA; Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden., Omer H; Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan.
Source: Magma (New York, N.Y.) [MAGMA] 2024 Oct; Vol. 37 (5), pp. 825-844. Date of Electronic Publication: 2023 Nov 18.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Springer Country of Publication: Germany NLM ID: 9310752 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1352-8661 (Electronic) Linking ISSN: 09685243 NLM ISO Abbreviation: MAGMA Subsets: MEDLINE
Imprint Name(s): Publication: 2003- : Heidelberg : Springer
Original Publication: New York, NY : Chapman & Hall, c1993-
MeSH Terms: Image Processing, Computer-Assisted*/methods , Magnetic Resonance Imaging*/methods , Heart*/diagnostic imaging , Data Compression*/methods , Algorithms*, Image Interpretation, Computer-Assisted/methods ; Humans ; Phantoms, Imaging ; Artifacts ; Signal-To-Noise Ratio ; Reproducibility of Results
Abstract: Background: Magnetic Resonance Imaging (MRI) is a highly demanded medical imaging system due to high resolution, large volumetric coverage, and ability to capture the dynamic and functional information of body organs e.g. cardiac MRI is employed to assess cardiac structure and evaluate blood flow dynamics through the cardiac valves. Long scan time is the main drawback of MRI, which makes it difficult for the patients to remain still during the scanning process.
Objective: By collecting fewer measurements, MRI scan time can be shortened, but this undersampling causes aliasing artifacts in the reconstructed images. Advanced image reconstruction algorithms have been used in literature to overcome these undersampling artifacts. These algorithms are computationally expensive and require a long time for reconstruction which makes them infeasible for real-time clinical applications e.g. cardiac MRI. However, exploiting the inherent parallelism in these algorithms can help to reduce their computation time.
Methods: Low-rank plus sparse (L+S) matrix decomposition model is a technique used in literature to reconstruct the highly undersampled dynamic MRI (dMRI) data at the expense of long reconstruction time. In this paper, Compressed Singular Value Decomposition (cSVD) model is used in L+S decomposition model (instead of conventional SVD) to reduce the reconstruction time. The results provide improved quality of the reconstructed images. Furthermore, it has been observed that cSVD and other parts of the L+S model possess highly parallel operations; therefore, a customized GPU based parallel architecture of the modified L+S model has been presented to further reduce the reconstruction time.
Results: Four cardiac MRI datasets (three different cardiac perfusion acquired from different patients and one cardiac cine data), each with different acceleration factors of 2, 6 and 8 are used for experiments in this paper. Experimental results demonstrate that using the proposed parallel architecture for the reconstruction of cardiac perfusion data provides a speed-up factor up to 19.15× (with memory latency) and 70.55× (without memory latency) in comparison to the conventional CPU reconstruction with no compromise on image quality.
Conclusion: The proposed method is well-suited for real-time clinical applications, offering a substantial reduction in reconstruction time.
(© 2023. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)
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Contributed Indexing: Keywords: Artifacts; CPU; CS; CUDA; FFT; GPU computing; OpenMP; cMRI; pMRI
Entry Date(s): Date Created: 20231118 Date Completed: 20241004 Latest Revision: 20241004
Update Code: 20260130
DOI: 10.1007/s10334-023-01128-5
PMID: 37978992
Database: MEDLINE
Description
ISSN:1352-8661
DOI:10.1007/s10334-023-01128-5