no code implementations • MIDL 2019 • Carolin M. Pirkl, Pedro A. Gómez, Ilona Lipp, Guido Buonincontri, Miguel Molina-Romero, Anjany Sekuboyina, Diana Waldmannstetter, Jonathan Dannenberg, Sebastian Endt, Alberto Merola, Joseph R. Whittaker, Valentina Tomassini, Michela Tosetti, Derek K. Jones, Bjoern H. Menze, Marion I. Menzel
Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues.
no code implementations • 26 Feb 2019 • Mohammad Golbabaee, Carolin M. Pirkl, Marion I. Menzel, Guido Buonincontri, Pedro A. Gómez
Deep learning (DL) has recently emerged to address the heavy storage and computation requirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprinting (MRF) reconstruction.
no code implementations • 6 Sep 2018 • Arnold Julian Vinoj Benjamin, Pedro A. Gómez, Mohammad Golbabaee, Tim Sprenger, Marion I. Menzel, Mike E. Davies, Ian Marshall
The main purpose of this study is to show that a highly accelerated Cartesian MRF scheme using a multi-shot EPI readout (i. e. multi-shot EPI-MRF) can produce good quality multi-parametric maps such as T1, T2 and proton density (PD) in a sufficiently short scan duration that is similar to conventional MRF.
no code implementations • 5 Sep 2018 • Mohammad Golbabaee, Dong-Dong Chen, Pedro A. Gómez, Marion I. Menzel, Mike E. Davies
Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy storage and computation requirements of a dictionary-matching (DM) step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications.