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A Deep-Learning Framework for Quantitative Magnetic Resonance Imaging (Case No. 2021-268)

Summary: Researchers from UCLA’s Departments of Bioengineering and Radiological Sciences have developed a novel deep learning framework for accelerated quantitative magnetic resonance imaging. Background: Fatty liver disease, or hepatic steatosis, affects nearly 2 billion people globally and has pernicious implications for deadlier diseases...

Published: 8/8/2024 | Inventor(s): Holden Wu, Shu-Fu Shih

Keywords(s): Deep Learning, fat quantification, IIM framework, Imaging, Medical Imaging, Metabolic/Endocrinology, MRI, multiparametric MRI (mpMRI), radial MRI, uncertainty estimation

Category(s): Medical Devices, Medical Devices > Medical Imaging, Medical Devices > Medical Imaging > MRI, Electrical, Electrical > Imaging, Software & Algorithms