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Highly Accelerated Multi-Dimensional MRI Using Physics-Guided Self-Supervised Deep Learning Reconstruction (Case No. 2026-164)
Summary: UCLA researchers in the Department of Radiological Sciences have developed a novel physics-guided, self-supervised deep learning framework for multi-dimensional magnetic resonance imaging that delivers superior image quality and preserves spatiotemporal fidelity at high acceleration rates. Background: Magnetic resonance imaging (MRI) is...
Published: 6/5/2026   |   Inventor(s): Xiaodong Zhong, Jun Lyu, John Finn
Keywords(s):  
Category(s): Software & Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning, Software & Algorithms > Image Processing, Software & Algorithms > Digital Health, Software & Algorithms > Data Analytics, Medical Devices, Medical Devices > Medical Imaging, Medical Devices > Medical Imaging > MRI