activity
20192022
most citedDeep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting

11 citations · 15 across the 4 of their papers we have counts for

collaborators

6 papers

eess.IV20221 cited

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…

eess.IV20221 cited

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…

physics.med-ph202011 cited

Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting

Carolin M. Pirkl, Pedro A. Gómez, Ilona Lipp +13

Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the match…

cs.CV2020

Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders

Mohammad Golbabaee, Guido Buonincontri, Carolin Pirkl +4

We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and dee…

physics.med-ph2020

Rapid three-dimensional multiparametric MRI with quantitative transient-state imaging

Pedro A. Gómez, Matteo Cencini, Mohammad Golbabaee +8

Novel methods for quantitative, transient-state multiparametric imaging are increasingly being demonstrated for assessment of disease and treatment efficacy. Here, we build on thes…

cs.CV20192 cited

Deep MR Fingerprinting with total-variation and low-rank subspace priors

Mohammad Golbabaee, Carolin M. Pirkl, Marion I. Menzel +2

Deep learning (DL) has recently emerged to address the heavy storage and computation requirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprinting (MR…