Artificial intelligence in 4D flow MRI: Review of technological aspects and clinical applications.
Authors
Affiliations (5)
Affiliations (5)
- Department of Smart Health Science and Technology, Kangwon National University, 1, Kangwondaehak-gil, Chuncheon 24341, Republic of Korea.
- Institute of Medical Devices, Kangwon National University, 1, Kangwondaehak-gil, Chuncheon 24341, Republic of Korea; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Flonics Co., Ltd., 1, Kangwondaehak-gil, Chuncheon 24341, Republic of Korea.
- Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation, 80, Cheombok-ro, Dong-gu, Daegu 41061, Republic of Korea.
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea.
- Department of Smart Health Science and Technology, Kangwon National University, 1, Kangwondaehak-gil, Chuncheon 24341, Republic of Korea; Flonics Co., Ltd., 1, Kangwondaehak-gil, Chuncheon 24341, Republic of Korea. Electronic address: [email protected].
Abstract
Four-dimensional (4D) flow magnetic resonance imaging (MRI) has evolved into an advanced non-invasive imaging technique that enables comprehensive assessment of blood flow in cardiovascular system. Hemodynamic data from 4D flow MRI provide key biomarkers such as wall shear stress (WSS), turbulent kinetic energy (TKE), and viscous energy loss, which aid in the analysis of cardiovascular diseases. However, its clinical application is limited by challenges such as prolonged scan times and limited spatiotemporal resolution. Conventional algorithms have been developed to automate the reconstruction of sparse data and perform image segmentation for hemodynamic quantification, but these methods are often time-consuming and require user expertise for accurate postprocessing. To address these limitations, artificial intelligence (AI), particularly deep learning (DL) techniques, has been introduced. DL models have shown promise in accelerating scan times by reconstructing sparsely sampled data into fully sampled datasets and enhancing image resolution by combining computational fluid dynamics (CFD) with 4D flow MRI data, as well as improving data quality through noise reduction. In addition, automated segmentation techniques have been developed to reduce user intervention, enabling more consistent and efficient analysis. Many researchers are working on DL-based approaches to 4D flow MRI using limited datasets. Recently, the lack of systematic methodologies has made it difficult to identify appropriate approaches. This paper aims to provide a comprehensive review of the latest AI applications using 4D flow MRI, enabling researchers to access and evaluate existing research more effectively. This review explores the latest research on integrating AI with 4D flow MRI, covering the entire process from data acquisition to postprocessing, and emphasizes the need for novel AI-based techniques to enhance clinical applicability. Furthermore, this review suggests it can serve as a foundation for developing innovative strategies toward fully automated AI-based approaches.