Accurate 3D contrast-free myocardial infarction delineation using a 4D dual-stream spatiotemporal feature learning framework
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Dosyalar
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Accurate 3D contrast-free myocardial infarction (MI) delineation has the potential to eliminate the need for toxic injections, thereby significantly advances diagnosis and treatment of MI. In this study, we propose a 4D dual-stream spatiotemporal feature learning framework (4D-DSS) that enables learning of 4D (3D + T) representation of the heart to accurately map the 3D MI regions, thereby directly delineating of 3D MI without contrast agent. This framework creatively introduces a dual-stream 3D spatiotemporal point cloud architecture enables to learn the myocardial 4D representation in both local and global aspects, and improve the comprehension and precision of the representation. Specifically, the framework utilizes the local spatiotemporal variation of individual point clouds to characterize minute distortions in myocardial regions and the global spatiotemporal variation of point cloud sequences to represent the overall myocardial motion between frames, thereby enables comprehensive learning of 3D myocardial motion and leverages these features to classify myocardial tissue into MI regions and normal regions. 4D-DSS significantly improved performance (with a precision increase of at least 4%) compared to four advanced methods. The results support the impact of our 4D-DSS framework on the development and implementation of 3D contrast-free myocardial infarction region delineation technology.& COPY; 2023 Elsevier B.V. All rights reserved.
Açıklama
This work was supported in part by The National Natural Science Foundation of China (62106001, U1908211), The University Synergy Innovation Program of Anhui Province, China (GXXT-2021-007), and The Anhui Provincial Natural Science Foundation, China (2208085Y19).
Anahtar Kelimeler
Myocardial Infarction, Contrast-free, 4D-Spatiotemporal Feature Learning, Point Cloud, Scene Flow
Kaynak
Applied Soft Computing
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
146
Sayı
Künye
Liu, J., Zhu, X., Xu, C., Xu, L., Gao, Z., Polat, K., & Alenezi, F. (2023). Accurate 3D contrast-free myocardial infarction delineation using a 4D dual-stream spatiotemporal feature learning framework. Applied Soft Computing, 146, 110694.
