MRI Denoising Using Deep Learning

Jose V. Manjon (1) , Pierrick Coupé (2, 3)

1 ITACA
2 LaBRI - Laboratoire Bordelais de Recherche en Informatique
3 PICTURA - Patch-based processing for medical and natural images

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.

Domaines: Imagerie médicale

Jose V. Manjon, Pierrick Coupé. MRI Denoising Using Deep Learning. International Workshop on Patch-based Techniques in Medical Imaging (MICCAI), Sep 2018, Granada, Spain. pp.12 - 19, ⟨10.1007/978-3-030-00500-9_2⟩. ⟨hal-01918437⟩