Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications

Journal of Imaging

Alexander Denker¹  Maximilian Schmidt¹  Johannes Leuschner¹  Peter Maass¹  Maureenvan Eijnatten²  Vladyslav Andriiashen²  SophiaBethany Coban²  PoulamiSomanya Ganguly²  KeesJoost Batenburg²  Dominik Bauer³  Amir Hadjifaradji⁴ 

[1] Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany;Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands;Computer Assisted Clinical Medicine, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany;School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada;

关键词: computed tomography (CT);    image reconstruction;    low-dose;    sparse-angle;    deep learning;    quantitative comparison;   

DOI  :  10.3390/jimaging7030044

来源: DOAJ

【 摘 要 】

The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a data challenge that we organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint. We focus on two applications of CT, namely, low-dose CT and sparse-angle CT. This enables us to fairly compare different methods using standardized settings. As a general result, we observe that the deep learning-based methods are able to improve the reconstruction quality metrics in both CT applications while the top performing methods show only minor differences in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). We further discuss a number of other important criteria that should be taken into account when selecting a method, such as the availability of training data, the knowledge of the physical measurement model and the reconstruction speed.

【 授权许可】

Unknown