A neural regression framework for low-dose coronary CT angiography (CCTA) denoising

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

*Corresponding author for this work

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

Abstract

In the last decade, the technological progress of multi-slice CT imaging has turned CCTA into a valuable tool for coronary assessment in many low to medium risk patients. Nevertheless, CCTA protocols expose the patient to high radiation doses, imposed by image quality and multiple cardiac phase acquisition requirements. Widespread use of CCTA calls for significant reduction of radiation exposure while maintaining high image quality as required for coronary assessment. Denoising algorithms have been recently applied to low-dose CT scans after image reconstruction. In this work, a fast neural regression framework is proposed for the denoising of low-dose CCTA. For this purpose, regression networks are trained to synthesize high-SNR patches directly from low-SNR input patches. In contrast to published methods, the denoising network is trained on real noise directly learned from noisy CT data rather than assuming a known parametric noise model. The denoised value for each pixel is computed as a function of the synthesized patches overlapping the pixel. The proposed algorithm is compared to state-of-the-art published algorithms for synthetic and real noise. The feature similarity index (FSIM) achieved by the proposed method is superior in all the comparisons with other methods, for synthetic radiation dose reductions higher than 90%. The results are further supported qualitatively, by observing a significant improvement in subsequent coronary reconstruction performed by commercial software on denoised images. The fast and high quality denoising capability suggests the proposed algorithm as a promising method for low-dose CCTA denoising.

Original languageEnglish
Title of host publicationPatch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings
EditorsYiqiang Zhan, Wenjia Bai, Guorong Wu, Pierrick Coupe, Brent C. Munsell, Gerard Sanroma
PublisherSpringer Verlag
Pages102-110
Number of pages9
ISBN (Print)9783319674339
DOIs
StatePublished - 2017
Event3rd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2017 held in conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 14 Sep 201714 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10530 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2017 held in conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Country/TerritoryCanada
CityQuebec City
Period14/09/1714/09/17

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