Work

Deep Learning Automated Measurement of Shunt Severity with Estimation of Uncertainty in 4D Flow MRI

Year: 2026

Type: article

Abstract: A fully automated deep learning–based system can be trained to measure systemic and pulmonary blood flow and shunt fraction using four-dimensional flow MRI with accuracy and reliability similar to that of expert physicians.

Source: Radiology Cardiothoracic Imaging

DOI: 10.1148/ryct.250138

Authors: Akhilesh R. R. Yeluru, Arielle Tycko, Noe Cazares, Evan M. Masutani, Roshun R. Sankaran, Parker M. Rushworth, Kent M. Hall, Lily Sung, Albert Hsiao

Institutions: University of California San Diego, University of California, Irvine, Wake Forest University Health Sciences, Wake Forest University

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of General Medical Sciences, National Institute of Biomedical Imaging and Bioengineering

Awards: G214752006, G398906001, Medical Scientist Training Program, G9000014410, Training in Bioengineering Research and Technology Development in Cardiovascular in Cardiopulmonary Health and Disease, UC San Diego Medical Scientist Training Program

OpenAlex ID: W7130512069

FWCI:

Cites: 24

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