4 pages, 1 figure, technical report for participation in the fastMRI 2020 challengeWe present a modular cross-domain neural network the XPDNet and its application to the MRI reconstruction task. This approach consists in unrolling the PDHG algorithm as well as learning the acceleration scheme between steps. We also adopt state-of-the-art techniques specific to Deep Learning for MRI reconstruction. At the time of writing, this approach is the best performer in PSNR on the fastMRI leaderboards for both knee and brain at acceleration factor 4
The clinical analysis of magnetic resonance (MR) can be accelerated through the undersampling in the...
International audienceMulti-contrast (MC) MR images are similar in structure and can leverage anatom...
Abstract(#br)Compressive sensing enables fast magnetic resonance imaging (MRI) reconstruction with u...
4 pages, 1 figure, technical report for participation in the fastMRI 2020 challengeWe present a modu...
A short version of this work has been accepted to the 17th International Symposium on Biomedical Ima...
This thesis aims to develop an efficient, deep network based method for Magnetic Resonance Imaging (...
This thesis aims to develop an efficient, deep network based method for Magnetic Resonance Imaging (...
Magnetic Resonance Imaging (MRI) is one of the most prominent imaging techniques in the world. Its m...
Magnetic resonance image (MRI) is a widely used non-invasive radiation-free imaging technique that u...
International audienceThe MRI reconstruction field lacked a proper data set that allowed for reprodu...
This work is an extended version of the work presented at the 2021 ISBI conference.International aud...
Deep learning networks have shown promising results in fast magnetic resonance imaging (MRI) reconst...
In this paper, a deep learning method for accelerating magnetic resonance imaging (MRI) is presented...
Purpose: To propose COMPaS, a learning-free Convolutional Network, that combines Deep Image Prior (D...
MRI is an imaging technology that non-invasively obtains high-quality medical images for diagnosis. ...
The clinical analysis of magnetic resonance (MR) can be accelerated through the undersampling in the...
International audienceMulti-contrast (MC) MR images are similar in structure and can leverage anatom...
Abstract(#br)Compressive sensing enables fast magnetic resonance imaging (MRI) reconstruction with u...
4 pages, 1 figure, technical report for participation in the fastMRI 2020 challengeWe present a modu...
A short version of this work has been accepted to the 17th International Symposium on Biomedical Ima...
This thesis aims to develop an efficient, deep network based method for Magnetic Resonance Imaging (...
This thesis aims to develop an efficient, deep network based method for Magnetic Resonance Imaging (...
Magnetic Resonance Imaging (MRI) is one of the most prominent imaging techniques in the world. Its m...
Magnetic resonance image (MRI) is a widely used non-invasive radiation-free imaging technique that u...
International audienceThe MRI reconstruction field lacked a proper data set that allowed for reprodu...
This work is an extended version of the work presented at the 2021 ISBI conference.International aud...
Deep learning networks have shown promising results in fast magnetic resonance imaging (MRI) reconst...
In this paper, a deep learning method for accelerating magnetic resonance imaging (MRI) is presented...
Purpose: To propose COMPaS, a learning-free Convolutional Network, that combines Deep Image Prior (D...
MRI is an imaging technology that non-invasively obtains high-quality medical images for diagnosis. ...
The clinical analysis of magnetic resonance (MR) can be accelerated through the undersampling in the...
International audienceMulti-contrast (MC) MR images are similar in structure and can leverage anatom...
Abstract(#br)Compressive sensing enables fast magnetic resonance imaging (MRI) reconstruction with u...