no code implementations • 23 Nov 2022 • Ketan Fatania, Kwai Y. Chau, Carolin M. Pirkl, Marion I. Menzel, Peter Hall, Mohammad Golbabaee
This paper proposes NonLinear Equivariant Imaging (NLEI), a self-supervised learning approach to eliminate the need for ground truth for deep MRF image reconstruction.
1 code implementation • 10 Feb 2022 • Ketan Fatania, Carolin M. Pirkl, Marion I. Menzel, Peter Hall, Mohammad Golbabaee
This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process.
no code implementations • 17 Jan 2022 • Yang Nan, Javier Del Ser, Simon Walsh, Carola Schönlieb, Michael Roberts, Ian Selby, Kit Howard, John Owen, Jon Neville, Julien Guiot, Benoit Ernst, Ana Pastor, Angel Alberich-Bayarri, Marion I. Menzel, Sean Walsh, Wim Vos, Nina Flerin, Jean-Paul Charbonnier, Eva van Rikxoort, Avishek Chatterjee, Henry Woodruff, Philippe Lambin, Leonor Cerdá-Alberich, Luis Martí-Bonmatí, Francisco Herrera, Guang Yang
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness.
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.