32 citations · 67 across the 14 of their papers we have counts for
5 papers · 1 filter
Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI
Ketan Fatania, Kwai Y. Chau, Carolin M. Pirkl +3
Current state-of-the-art reconstruction for quantitative tissue maps from fast, compressive, Magnetic Resonance Fingerprinting (MRF), use supervised deep learning, with the drawbac…
A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers
Ketan Fatania, Carolin M. Pirkl, Marion I. Menzel +2
Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern whic…
Deep Unrolling for Magnetic Resonance Fingerprinting
Dongdong Chen, Mike E. Davies, Mohammad Golbabaee
Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative MR imaging approach. Deep learning methods have been proposed for MRF and demonstrated improved perf…
Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations
Dongdong Chen, Mike E. Davies, Mohammad Golbabaee
Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems. This consistency is mostly un-controlled in the current…
A Fully Convolutional Network for MR Fingerprinting
Dongdong Chen, Mohammad Golbabaee, Pedro A. Gomez +2
Magnetic Resonance Fingerprinting (MRF) methods typically rely on dictionary matching to map the temporal MRF signals to quantitative tissue parameters. These methods suffer from h…