7 citations · 7 across the 2 of their papers we have counts for
5 papers
Multi-compartment diffusion MRI, T2 relaxometry and myelin water imaging as neuroimaging descriptors for anomalous tissue detection
Elda Fischi-Gomez, Jonathan Rafael-Patino, Marco Pizzolato +5
Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease characterized by diffuse and focal areas of tissue loss. Conventional MRI techniques such as T1-weighted an…
T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions
Hélène Lajous, Tom Hilbert, Christopher W. Roy +12
Relaxometry studies in preterm and at-term newborns have provided insight into brain microstructure, thus opening new avenues for studying normal brain development and supporting d…
Model-Informed Machine Learning for Multi-component T2 Relaxometry
Thomas Yu, Erick Jorge Canales Rodriguez, Marco Pizzolato +9
Recovering the T2 distribution from multi-echo T2 magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-st…
Compressed Sensing with Signal Averaging for Improved Sensitivity and Motion Artifact Reduction in Fluorine-19 MRI
Emeline Darçot, Jérôme Yerly, Tom Hilbert +5
Fluorine-19 (19F) MRI of injected perfluorocarbon emulsions (PFCs) allows for the non-invasive quantification of inflammation and cell tracking, but suffers from a low signal-to-no…
Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis
Francesco La Rosa, Mário João Fartaria, Tobias Kober +4
In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patien…