activity
20182022
most citedMR-Based Electrical Property Reconstruction Using Physics-Informed Neural Networks

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

cs.LG20221 cited

MR-Based Electrical Property Reconstruction Using Physics-Informed Neural Networks

Xinling Yu, José E. C. Serrallés, Ilias I. Giannakopoulos +4

Electrical properties (EP), namely permittivity and electric conductivity, dictate the interactions between electromagnetic waves and biological tissue. EP can be potential biomark…

physics.med-ph2020

Magnetic-resonance-based electrical property mapping using Global Maxwell Tomography with an 8-channel head coil at 7 Tesla: a simulation study

Ilias I. Giannakopoulos, José E. C. Serrallés, Luca Daniel +4

Objective: Global Maxwell Tomography (GMT) is a recently introduced volumetric technique for noninvasive estimation of electrical properties (EP) from magnetic resonance measuremen…

physics.ins-det2018

The Optimality Principle for MR signal excitation and reception: New physical insights into ideal radiofrequency coil design

Daniel K. Sodickson, Riccardo Lattanzi, Manushka Vaidya +4

Purpose: Despite decades of collective experience, radiofrequency coil optimization for MR has remained a largely empirical process, with clear insight into what might constitute t…

physics.med-ph2018

Hybrid-State Free Precession in Nuclear Magnetic Resonance

Jakob Assländer, Dmitry S. Novikov, Riccardo Lattanzi +2

The dynamics of large spin-1/2 ensembles in the presence of a varying magnetic field are commonly described by the Bloch equation. Most magnetic field variations result in unintuit…

physics.med-ph2018

Multicompartment Magnetic Resonance Fingerprinting

Sunli Tang, Carlos Fernandez-Granda, Sylvain Lannuzel +5

Magnetic resonance fingerprinting (MRF) is a technique for quantitative estimation of spin-relaxation parameters from magnetic-resonance data. Most current MRF approaches assume th…