Search Results - radial+mri

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Method and Apparatus for Segmentation of Dense MR Images Using Deep Learning with Domain Adaptation (Case No. 2025-208)

Summary: UCLA researchers from the Department of Radiological Sciences have developed a deep learning-based segmentation framework that enables fully automated and reproducible analysis of left ventricular (LV) function in Displacement Encoding with Stimulated Echoes (DENSE) MRI. Background: Myocardial strain is a key metric for analyzing how much...

Published: 8/15/2025 | Inventor(s): Xiaodong Zhong, Siyue Li, Kim-Lien Nguyen

Keywords(s): acute myocardial infarction, adjustable mount, aneurysm drainage, automatic leveling, bedside decisions, blood pressure monitoring, cardiac cycle, Cardiac Electrophysiology, Cardiac failure, Cardiac Magnetic Resonance Imaging, Cardiac MRI, Cardiovascular, Cardiovascular Disease, Cardiovascular Disease Nephropathy, cardiovascular diseases, cardiovascular monitoring, cerebrospinal fluid (CSF) drainage, Cine MRI, clamp, critical-care, CSF drainage, Deep learning MRI, DENSE MRI, laser alignment, Left ventricular segmentation, lumbar drains, Motion analysis, MRI, multiparametric MRI (mpMRI), Myocardial strain, non-invasive cardiac monitoring, operator variability, passive reflective target, phlebostatic axis measurement, Pseudo-labeling, radial MRI, Segment Anything Model (SAM), self-leveling system, transducer alignment, Unsupervised domain adaptation (UDA)

Category(s): Medical Devices > Cardiac, Electrical > Imaging, Software & Algorithms > AI Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning, Software & Algorithms > Image Processing, Therapeutics > Cardiovascular, Therapeutics > Radiology, Medical Devices > Medical Imaging > MRI, Medical Devices > Medical Imaging

Methods and Systems for Low-Cost Medical Image Annotation Using Non-experts (Case No. 2025-108)

Summary: UCLA researchers in the Department of Electrical and Computer Engineering have developed an AI-based interface designed to enable individuals without specialized training to identify arthritis in medical imaging. Background: The use of artificial intelligence (AI) for medical imaging analysis holds great promise for the future of healthcare....

Published: 7/23/2025 | Inventor(s): Xiang Chen, Youngseung Jeon, Christopher Hwang

Keywords(s): 3D tissue imaging, AI-guided diagnostics, AI-guided medical imaging, AI-guided medical intervention, arthritis, Artifical Intelligence (Machine Learning, Data Mining), Artificial Intelligence, artificial intelligence algorithms, artificial intelligence augmentation, artificial intelligence/machine learning models, Artificial Neural Network, bioimaging, Computer-Aided Diagnosis, computer-aided radiology, Diagnostic Markers & Platforms, Diagnostic Test, diagnostics, generative artificial intelligence, Image Analysis, Image Resolution, Imaging, infrared thermal imaging, Machine Learning, machine learning modeling, machine perception, Magnetic Resonance Imaging Medical Physics, Magnetic Resonance Imaging Pathology, Medical artificial intelligence (AI), Medical diagnostics, Medical Imaging, Microscopy And Imaging, non-invasive imaging, osteoarthritis, radial MRI, radiologic imaging, Radiology, Radiology / Radiomitigation, radiosurgery

Category(s): Software & Algorithms, Software & Algorithms > AI Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning, Software & Algorithms > Digital Health, Software & Algorithms > Image Processing, Life Science Research Tools, Life Science Research Tools > Lab Equipment, Life Science Research Tools > Microscopy And Imaging, Medical Devices, Medical Devices > Medical Imaging, Medical Devices > Monitoring And Recording Systems, Therapeutics, Therapeutics > Musculoskeletal Disease, Therapeutics > Radiology

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: 7/17/2025 | 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