Learning real noise for ultra-low dose lung CT denoising

Michael Green, Edith M. Marom, Eli Konen, Nahum Kiryati, Arnaldo Mayer*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Neural image denoising is a promising approach for quality enhancement of ultra-low dose (ULD) CT scans after image reconstruction. The availability of high-quality training data is instrumental to its success. Still, synthetic noise is generally used to simulate the ULD scans required for network training in conjunction with corresponding normal dose scans. This reductive approach may be practical to implement but ignores any departure of the real noise from the assumed model. In this paper, we demonstrate the training of denoising neural networks with real noise. For this purpose, a special training set is created from a pair of ULD and normal-dose scans acquired on each subject. Accurate deformable registration is computed to ensure the required pixel-wise overlay between corresponding ULD and normal-dose patches. To our knowledge, it is the first time real CT noise is used for the training of denoising neural networks. The benefits of the proposed approach in comparison to synthetic noise training are demonstrated both qualitatively and quantitatively for several state-of-the art denoising neural networks. The obtained results prove the feasibility and applicability of real noise learning as a way to improve neural denoising of ULD lung CT.

Original languageEnglish
Title of host publicationPatch-Based Techniques in Medical Imaging - 4th International Workshop, Patch-MI 2018, Held in Conjunction with MICCAI 2018, Proceedings
EditorsBrent C. Munsell, Guorong Wu, Pierrick Coupé, Gerard Sanroma, Yiqiang Zhan, Wenjia Bai
PublisherSpringer Verlag
Pages3-11
Number of pages9
ISBN (Print)9783030004996
DOIs
StatePublished - 2018
Event4th International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 20 Sep 201820 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11075 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Country/TerritorySpain
CityGranada
Period20/09/1820/09/18

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