AUTOMATIC TOOL FOR PULMONARY ARTERY HEMODYNAMIC ASSESSMENT FROM 4D FLOW MRI

被引：1

作者：

Bobeda, Javier ^{[1
]}

Erostarbe, Haizea ^{[1
]}

Stephens, Maialen ^{[1
,2
,3
]}

Gaitan, Angel ^{[4
,5
]}

Kumar, Rahul ^{[6
]}

Nuche, Jorge ^{[4
,5
]}

Marco, Irene ^{[4
,5
]}

Delgado, Juan ^{[4
,5
]}

Ruiz-Cabello, Jesus ^{[6
,7
]}

Lopez-Linares, Karen ^{[1
,3
]}

机构：

[1] Basque Res & Technol Alliance, Vicomtech, Mendaro, Spain

[2] Univ Pompeu Fabra, BCN Medtech, Barcelona, Spain

[3] Biodonostia Hlth Res Inst, San Sebastian, Spain

[4] Hosp 12 Octubre, Serv Cardiol, Madrid, Spain

[5] CIBERCV, Madrid, Spain

[6] Basque Res & Technol Alliance, CIC biomaGUNE, Mendaro, Spain

[7] Univ Complutense Madrid, Dept Quim Ciencias Farmaceut, Basque Fdn Sci, CIBERES,Ikerbasque, Madrid, Spain

来源：

2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI | 2023年

关键词：

deep learning; computational fluid dynamics; 4D flow MRI; automatic blood flow biomarkers;

D O I：

10.1109/ISBI53787.2023.10230426

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Currently, the extraction of blood flow biomarkers to characterize diseases is a time consuming process as it requires the manual segmentation of vascular structures and complex computational fluid dynamics (CFD) simulations. Here, we propose a tool to automatically segment the pulmonary artery and to compute biomarkers from 4D flow magnetic resonance images. In the context of Pulmonary Hypertension (PH), we show that biomarkers such as peak velocity, flow rate, helicity and vorticity provide discriminative power between patient groups and are derived in a faster and simpler way than traditional methods.

引用

页数：5

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