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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
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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