18 citations · 36 across the 8 of their papers we have counts for
4 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…
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…
Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging
Pedro A. Gómez, Miguel Molina-Romero, Guido Buonincontri +2
Magnetic resonance imaging (MRI) is a remarkably powerful diagnostic technique: it generates wide-ranging information for the non-invasive study of tissue anatomy and physiology. C…