Four-dimensional flow magnetic resonance imaging (4D flow MRI) offers a powerful tool for visualizing fluid flows, critical for both diagnosing cardiovascular diseases and analyzing engineering fluid dynamics. Despite its potential in medical research, the clinical applicability of 4D flow MRI often faces challenges due to inherent noise. To mitigate this, we introduce the split-and-overlap singular value decomposition (SOSVD) filter, a distinctive noise reduction approach. Unlike traditional singular value decomposition methods, the SOSVD filter partitions the primary data matrix into overlapping subdomains and then applies singular value decomposition to each subdomain, preserving only the dominant mode for noise attenuation. Evaluations on simulated and experimental flow data within a square duct revealed a significant decrease in root mean square noise metrics. Moreover, when applied to in vivo aortic data, the SOSVD filter enhanced various flow determinants, including divergence, velocity gradients, streamlines, and velocity coherence. Thus, the SOSVD method presents a promising avenue for augmenting noise reduction in 4D flow MRI, potentially elevating diagnostic accuracy and enriching cardiovascular disease research.
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January 2024
Research Article|
January 16 2024
Denoising four-dimensional flow magnetic resonance imaging data using a split-and-overlap approach via singular value decomposition
Seungmin Kang (강승민)
;
Seungmin Kang (강승민)
(Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft)
1
Department of Mechanical Convergence Engineering, Hanyang University
, Seoul, South Korea
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Don-Gwan An (안돈관)
;
Don-Gwan An (안돈관)
(Data curation)
1
Department of Mechanical Convergence Engineering, Hanyang University
, Seoul, South Korea
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Hojin Ha (하호진)
;
Hojin Ha (하호진)
(Resources)
2
Department of Mechanical and Biomedical Engineering, Kangwon National University
, Chuncheon, South Korea
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Dong Hyun Yang (양동현)
;
Dong Hyun Yang (양동현)
(Resources, Writing – review & editing)
3
Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine
, Seoul, South Korea
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Ilhoon Jang (장일훈)
;
Ilhoon Jang (장일훈)
a)
(Methodology, Project administration, Supervision, Writing – review & editing)
1
Department of Mechanical Convergence Engineering, Hanyang University
, Seoul, South Korea
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Simon Song (송시몬)
Simon Song (송시몬)
a)
(Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review & editing)
1
Department of Mechanical Convergence Engineering, Hanyang University
, Seoul, South Korea
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Physics of Fluids 36, 011906 (2024)
Article history
Received:
October 13 2023
Accepted:
December 22 2023
Citation
Seungmin Kang, Don-Gwan An, Hojin Ha, Dong Hyun Yang, Ilhoon Jang, Simon Song; Denoising four-dimensional flow magnetic resonance imaging data using a split-and-overlap approach via singular value decomposition. Physics of Fluids 1 January 2024; 36 (1): 011906. https://doi.org/10.1063/5.0180996
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