The clinical analysis of magnetic resonance (MR) can be accelerated through the undersampling in the k-space (Fourier domain). Deep learning techniques have been recently received considerable interest for accelerating MR imaging (MRI). In this paper, a deep learning method for accelerating MRI is presented, which is able to reconstruct undersampled MR images obtained by reducing the k-space data in the direction of the phase encoding. In particular, we focus on the reconstruction of MR images related to patients affected by multiple sclerosis (MS) and we propose a new multimodal deep learning architecture that is able to accelerate the MRI up to 8 times, while providing a high quality of the reconstructed image, especially in the area of the brain lesions. Experiments have been performed on T2W and FLAIR images, both providing useful information for MS MRI, and have shown that the proposed multimodal network is able to achieve a higher reconstruction accuracy with respect to existing methods, while effectively reducing the execution time of the clinical analysis.

A multimodal dense U-Net for accelerating multiple sclerosis MRI / Falvo, A.; Comminiello, D.; Scardapane, S.; Scarpiniti, M.; Uncini, A.. - (2019), pp. 1-6. (Intervento presentato al convegno 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019 tenutosi a Pittsburgh, PA, USA) [10.1109/MLSP.2019.8918781].

A multimodal dense U-Net for accelerating multiple sclerosis MRI

Comminiello D.
;
Scardapane S.;Scarpiniti M.;Uncini A.
2019

Abstract

The clinical analysis of magnetic resonance (MR) can be accelerated through the undersampling in the k-space (Fourier domain). Deep learning techniques have been recently received considerable interest for accelerating MR imaging (MRI). In this paper, a deep learning method for accelerating MRI is presented, which is able to reconstruct undersampled MR images obtained by reducing the k-space data in the direction of the phase encoding. In particular, we focus on the reconstruction of MR images related to patients affected by multiple sclerosis (MS) and we propose a new multimodal deep learning architecture that is able to accelerate the MRI up to 8 times, while providing a high quality of the reconstructed image, especially in the area of the brain lesions. Experiments have been performed on T2W and FLAIR images, both providing useful information for MS MRI, and have shown that the proposed multimodal network is able to achieve a higher reconstruction accuracy with respect to existing methods, while effectively reducing the execution time of the clinical analysis.
2019
29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019
deep neural network; fast MRI; magnetic resonance imaging; multimodal dense U-Net
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A multimodal dense U-Net for accelerating multiple sclerosis MRI / Falvo, A.; Comminiello, D.; Scardapane, S.; Scarpiniti, M.; Uncini, A.. - (2019), pp. 1-6. (Intervento presentato al convegno 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019 tenutosi a Pittsburgh, PA, USA) [10.1109/MLSP.2019.8918781].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1354459
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