TY - GEN
T1 - Stochastic 4D Flow Vector-Field Signatures
T2 - 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
AU - Elbaz, Mohammed S.M.
AU - Malaisrie, Chris
AU - McCarthy, Patrick
AU - Markl, Michael
N1 - Funding Information:
Acknowledgements. The first author’s research is supported in part by Transformational Project Award AHA 20TPA35490311 from the American Heart Association (AHA), and grant R21 HL150498 from the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH-NHLBI).
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - 4D Flow MRI
KW - Blood flow quantification
KW - Vector-field
UR - http://www.scopus.com/inward/record.url?scp=85116507807&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-87240-3_21
DO - 10.1007/978-3-030-87240-3_21
M3 - Conference contribution
AN - SCOPUS:85116507807
SN - 9783030872397
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 215
EP - 224
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
A2 - de Bruijne, Marleen
A2 - Cattin, Philippe C.
A2 - Cotin, Stéphane
A2 - Padoy, Nicolas
A2 - Speidel, Stefanie
A2 - Zheng, Yefeng
A2 - Essert, Caroline
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 27 September 2021 through 1 October 2021
ER -