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Current Directions in Biomedical Engineering

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Buzug, Thorsten M. / Haueisen, Jens / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Kraft, Marc / Lenarz, Thomas / Leonhardt, Steffen / Malberg, Hagen / Penzel, Thomas / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Urban, Gerald A.

Open Access
Online
ISSN
2364-5504
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Residual U-Net Convolutional Neural Network Architecture for Low-Dose CT Denoising

Mattias P. Heinrich / Maik Stille / Thorsten M. Buzug
Published Online: 2018-09-22 | DOI: https://doi.org/10.1515/cdbme-2018-0072

Abstract

Low-dose CT has received increasing attention in the recent years and is considered a promising method to reduce the risk of cancer in patients. However, the reduction of the dosage leads to quantum noise in the raw data, which is carried on in the reconstructed images. Two different multilayer convolutional neural network (CNN) architectures for the denoising of CT images are investigated. ResFCN is based on a fully-convolutional network that consists of three blocks of 5×5 convolutions filters and a ResUNet that is trained with 10 convolutional blocks that are arranged in a multi-scale fashion. Both architectures feature a residual connection of the input image to ease learning. Training images are based on realistic simulations by using the XCAT phantom. The ResUNet approach shows the most promising results with a peak signal to noise ratio of 44.00 compared to ResFCN with 41.79.

Keywords: Denoising; Low Dose CT; Phantom Simulation; Deep Learning; CNN

About the article

Published Online: 2018-09-22

Published in Print: 2018-09-01


Citation Information: Current Directions in Biomedical Engineering, Volume 4, Issue 1, Pages 297–300, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2018-0072.

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