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Found 3 papers, 1 papers with code
Generalizable synthetic MRI with physics-informed convolutional networks
In this study, we develop a physics-informed deep learning-based method to synthesize multiple brain magnetic resonance imaging (MRI) contrasts from a single five-minute acquisition and investigate its ability to generalize to arbitrary contrasts to accelerate neuroimaging protocols.
Towards retrospective motion correction and reconstruction for clinical 3D brain MRI protocols with a reference contrast
Motion artifacts often spoil the radiological interpretation of MR images, and in the most severe cases the scan needs be repeated, with additional costs for the provider.
Dictionary-free MR Fingerprinting reconstruction of balanced-GRE sequences
In this work, a method is presented to reconstruct balanced gradient-echo (GRE) acquisitions with established iterative algorithms for nonlinear least-squares, thus bypassing the dictionary computation and the exhaustive search.
Medical Physics
