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Purpose
To develop a model‐guided self‐supervised deep learning MRI reconstruction framework called reference‐free latent map extraction (RELAX) for rapid quantitative MR parameter mapping.
Methods
Two physical models are incorporated for network training in RELAX, including the inherent MR imaging model and a quantitative model that is used to fit parameters in quantitative MRI. By enforcing these...
Purpose
To develop and evaluate a novel deep learning‐based image reconstruction approach called MANTIS (Model‐Augmented Neural neTwork with Incoherent k‐space Sampling) for efficient MR parameter mapping.
Methods
MANTIS combines end‐to‐end convolutional neural network (CNN) mapping, incoherent k‐space undersampling, and a physical model as a synergistic framework. The CNN mapping directly converts...
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