MRI Denoising Using Deep Learning

Jose Manjon 1 Pierrick Coupé 2, 3
3 PICTURA
LaBRI - Laboratoire Bordelais de Recherche en Informatique
Abstract : MRI denoising is a classical preprocessing step which aims at reducing the noise naturally present in MR images. In this paper, we present a new method for MRI denoising that combines recent advances in deep learning with classical approaches for noise reduction. Specifically, the proposed method follows a two-stage strategy. The first stage is based on an overcomplete patch-based convolutional neural network that blindly removes the noise without estimation of local noise level present in the images. The second stage uses this filtered image as a guide image within a rotationally invariant non-local means filter. The proposed approach has been compared with related state-of-the-art methods and showed competitive results in all the studied cases.
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Article dans une revue
Lecture Notes in Computer Science, Springer, 2018, pp.12 - 19. 〈10.1007/978-3-030-00500-9_2〉
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https://hal.archives-ouvertes.fr/hal-01918437
Contributeur : Pierrick Coupé <>
Soumis le : dimanche 25 novembre 2018 - 09:55:35
Dernière modification le : lundi 26 novembre 2018 - 01:02:56

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Jose Manjon, Pierrick Coupé. MRI Denoising Using Deep Learning. Lecture Notes in Computer Science, Springer, 2018, pp.12 - 19. 〈10.1007/978-3-030-00500-9_2〉. 〈hal-01918437〉

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