11 citations · 20 across the 5 of their papers we have counts for
6 papers
StoDIP: Efficient 3D MRF image reconstruction with deep image priors and stochastic iterations
Perla Mayo, Matteo Cencini, Carolin M. Pirkl +4
Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI for multiparametric tissue mapping. The reconstruction of quantitative maps requires tailor…
Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders
Perla Mayo, Matteo Cencini, Ketan Fatania +5
The estimation of multi-parametric quantitative maps from Magnetic Resonance Fingerprinting (MRF) compressed sampled acquisitions, albeit successful, remains a challenge due to the…
Pattern-matching Unit for Medical Applications
O. Leombruni, A. Annovi, P. Giannetti +8
We explore the application of concepts developed in High Energy Physics (HEP) for advanced medical data analysis. Our study case is a problem with high social impact: clinically-fe…
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…
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…
An Aristotelian view on MR-based attenuation correction (ARISTOMRAC): combining the four elements
Matteo Cencini, Michela Tosetti, Guido Buonincontri
MR-based attenuation correction (MRAC) is important for accurate quantification of the uptake of PET tracers in combined PET/MR scanners. However, current techniques for MRAC usual…