Stochastic 4D Flow Vector-Field Signatures: A New Approach for Comprehensive 4D Flow MRI Quantification

Mohammed S.M. Elbaz*, Chris Malaisrie, Patrick McCarthy, Michael Markl

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

4D Flow MRI has emerged as a new imaging technique for assessing 3D flow dynamics in the heart and great arteries (e.g., Aorta) in vivo. 4D Flow MRI provides in vivo voxel-wise mapping of 3D time-resolved three-directional velocity vector-field information. However, current techniques underutilize such comprehensive vector-field information by reducing it to aggregate or derivative scalar-field. Here we propose a new data-driven stochastic methodological approach to derive the unique 4D vector-field signature of the 3D flow dynamics. Our technique is based on stochastically encoding the profile of the underlying pair-wise vector-field associations comprising the entire 3D flow-field dynamics. The proposed technique consists of two stages: 1) The 4D Flow vector-field signature profile is constructed by stochastically encoding the probability density function of the co-associations of millions of pair-wise vectors over the entire 4D Flow MRI domain. 2) The Hemodynamic Signature Index (HSI) is computed as a measure of the degree of alteration in the 4D Flow signature between patients. The proposed technique was extensively evaluated in three in vivo 4D Flow MRI datasets of 106 scans, including 34 healthy controls, 57 bicuspid aortic valve (BAV) patients and 15 Rescan subjects. Results demonstrate our technique’s excellent robustness, reproducibility, and ability to quantify distinct signatures in BAV patients.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
EditorsMarleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
PublisherSpringer Science and Business Media Deutschland GmbH
Pages215-224
Number of pages10
ISBN (Print)9783030872397
DOIs
StatePublished - 2021
Event24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: Sep 27 2021Oct 1 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12905 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period9/27/2110/1/21

Keywords

  • 4D Flow MRI
  • Blood flow quantification
  • Vector-field

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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