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Towards multi‐modal data fusion for super‐resolution and denoising of 4D‐Flow MRI
Author(s) -
PerezRaya Isaac,
Fathi Mojtaba F.,
Baghaie Ahmadreza,
Sacho Raphael H.,
Koch Kevin M.,
D'Souza Roshan M.
Publication year - 2020
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.3381
Subject(s) - computational fluid dynamics , flow (mathematics) , noise (video) , computer science , noise reduction , projection (relational algebra) , temporal resolution , magnetic resonance imaging , algorithm , dynamic mode decomposition , computer vision , sensor fusion , artificial intelligence , mathematics , physics , geometry , mechanics , optics , machine learning , radiology , image (mathematics) , medicine
4D‐Flow magnetic resonance imaging (MRI) has enabled in vivo time‐resolved measurement of three‐dimensional blood flow velocities in the human vascular system. However, its clinical use has been hampered by two main issues, namely, low spatio‐temporal resolution and acquisition noise. While patient‐specific computational fluid dynamics (CFD) simulations can address the resolution and noise issues, its fidelity is impacted by accuracy of estimation of boundary conditions, model parameters, vascular geometry, and flow model assumptions. In this paper a scheme to address limitations of both modalities through data‐fusion is presented. The solutions of the patient‐specific CFD simulation are characterized using proper orthogonal decomposition (POD). Next, a process of projecting the 4D‐Flow MRI data onto the POD basis and projection coefficient mapping using generalized dynamic mode decomposition (DMD) enables simultaneous super‐resolution and denoising of 4D‐Flow MRI. The method has been tested using numerical phantoms derived from patient‐specific aneurysmal geometries and applied to in vivo 4D‐Flow MRI data.