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

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Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications Journal of Imaging Article Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications Johannes Leuschner 1,*,† , Maximilian Schmidt 1,† , Poulami Somanya Ganguly 2,3 , Vladyslav Andriiashen 2 , Sophia Bethany Coban 2 , Alexander Denker 1 , Dominik Bauer 4 , Amir Hadjifaradji 5 , Kees Joost Batenburg 2,6 , Peter Maass 1 and Maureen van Eijnatten 2,7,* 1 Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany; [email protected] (M.S.); [email protected] (A.D.); [email protected] (P.M.) 2 Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands; [email protected] (P.S.G.); [email protected] (V.A.); [email protected] (S.B.C.); [email protected] (K.J.B.) 3 The Mathematical Institute, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands 4 Computer Assisted Clinical Medicine, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; [email protected] 5 School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada; [email protected] 6 Citation: Leuschner, J.; Schmidt, M.; Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands 7 Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 3, Ganguly, P.S.; Andriiashen, V.; 5612 AE Eindhoven, The Netherlands Coban, S.B.; Denker, A.; Bauer, D.; * Correspondence: [email protected] (J.L.); [email protected] (M.v.E.) Hadjifaradji, A.; Batenburg, K.J.; † These authors contributed equally to this work. Maass, P.; et al. Quantitative Comparison of Deep Learning-Based Abstract: The reconstruction of computed tomography (CT) images is an active area of research. Image Reconstruction Methods for Following the rise of deep learning methods, many data-driven models have been proposed in recent Low-Dose and Sparse-Angle CT years. In this work, we present the results of a data challenge that we organized, bringing together Applications. J. Imaging 2021, 7, 44. https://doi.org/10.3390/jimaging algorithm experts from different institutes to jointly work on quantitative evaluation of several data- 7030044 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 Academic Editors: Yudong Zhang, using standardized settings. As a general result, we observe that the deep learning-based methods are Juan Manuel Gorriz and able to improve the reconstruction quality metrics in both CT applications while the top performing Zhengchao Dong 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 Received: 29 January 2021 account when selecting a method, such as the availability of training data, the knowledge of the Accepted: 22 February 2021 physical measurement model and the reconstruction speed. Published: 2 March 2021 Keywords: computed tomography (CT); image reconstruction; low-dose; sparse-angle; deep learning; Publisher’s Note: MDPI stays neutral quantitative comparison with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction Computed tomography (CT) is a widely used (bio)medical imaging modality, with various applications in clinical settings, such as diagnostics [1], screening [2] and virtual Copyright: c 2021 by the authors. Licensee MDPI, Basel, Switzerland. treatment planning [3,4], as well as in industrial [5] and scientific [6–8] settings. One of This article is an open access article the fundamental aspects of this modality is the reconstruction of images from multiple distributed under the terms and X-ray measurements taken from different angles. Because each X-ray measurement exposes conditions of the Creative Commons the sample or patient to harmful ionizing radiation, minimizing this exposure remains an Attribution (CC BY) license (https:// active area of research [9]. The challenge is to either minimize the dose per measurement or creativecommons.org/licenses/by/ the total number of measurements while maintaining sufficient image quality to perform 4.0/). subsequent diagnostic or analytic tasks. J. Imaging 2021, 7, 44. https://doi.org/10.3390/jimaging7030044 https://www.mdpi.com/journal/jimaging J. Imaging 2021, 7, 44 2 of 49 To date, the most common classical methods used for CT image reconstruction are filtered back-projection (FBP) and iterative reconstruction (IR) techniques. FBP is a sta- bilized and discretized version of the inverse Radon transform, in which 1D projections are filtered by the 1D Radon kernel (back-projected) in order to obtain a 2D signal [10,11]. FBP is very fast, but is not suitable for limited-data or sparse-angle setups, resulting in various imaging artifacts, such as streaking, stretching, blurring, partial volume effects, or noise [12]. Iterative reconstruction methods, on the other hand, are computationally inten- sive but are able to incorporate a priori information about the system during reconstruction. Many iterative techniques are based on statistical methods such as Markov random fields or regularization methods where the regularizers are designed and incorporated into the problem of reconstruction mathematically [13]. A popular choice for the regularizer is total variation (TV) [14,15]. Another well-known iterative method suitable for large-scale tomography problems is the conjugate gradient method applied to solve the least squares problem (CGLS) [16]. When classical techniques such as FBP or IR are used to reconstruct low-dose CT images, the image quality often deteriorates significantly in the presence of increased noise. Therefore, the focus is shifting towards developing reconstruction methods in which a single or multiple component(s), or even the entire reconstruction process is performed using deep learning [17]. Generally data-driven approaches promise fast and/or accurate image reconstruction by taking advantage of a large number of examples, that is, training data. The methods that learn parts of the reconstruction process can be roughly divided into learned regularizers, unrolled iterative schemes, and post-processing of reconstructed CT images. Methods based on learned regularizers work on the basis of learning convolutional filters from the training data that can subsequently be used to regularize the reconstruction problem by plugging into a classical iterative optimization scheme [18]. Unrolled iterative schemes go a step further in the sense that they “unroll” the steps of the iterative scheme into a sequence of operations where the operators are replaced with convolutional neural networks (CNNs). A recent example is the learned primal-dual algorithm proposed by Adler et al. [19]. Finally, various post-processing methods have been proposed that correct noisy images or those with severe artifacts in the image domain [20]. Examples are improving tomographic reconstruction from limited data using a mixed-scale dense (MS-D) CNN [21], U-Net [22] or residual encoder-decoder CNN (RED-CNN) [23], as well as CT image denoising techniques [24,25]. Somewhat similar are the methods that can be trained in a supervised manner to improve the measurement data in the sinogram domain [26]. The first fully end-to-end learned reconstruction method was the automated trans- form by the manifold approximation (AUTOMAP) algorithm [27] developed for magnetic resonance (MR) image reconstruction. This method directly learns the (global) relation between the measurement data and the image, that is, it replaces the Radon or Fourier transform with a neural network. The disadvantages of this approach are the large mem- ory requirements, as well as the fact that it might not be necessary to learn the entire transformation from scratch because an efficient analytical transform is already available. A similar approach for CT reconstruction was iRadonMAP proposed by He et al. [28], who developed an interpretable framework for Radon inversion in medical X-ray CT. In addition, Li et al. [29] proposed an end-to-end reconstruction framework for Radon inversion called iCT-Net, and demonstrated its advantages in solving sparse-view CT reconstruction problems. The aforementioned deep learning-based CT image reconstruction methods differ greatly in terms of which component of the reconstruction task is learned and in which domain the method operates (image or sinogram domain), as well as the computational and data-related requirements. As a result, it remains difficult to compare the performance of deep learning-based reconstruction methods across different imaging domains and applications. Thorough comparisons between different reconstruction methods are further complicated by the lack of sufficiently large benchmarking datasets, including ground truth J. Imaging 2021, 7, 44 3 of 49 reconstructions, for training, validation, and testing. CT manufacturers are typically very reluctant in making raw measurement data available for research purposes, and privacy regulations for making medical imaging data publicly available are becoming increasingly strict [30,31]. 1.1. Goal of This Study The aim of this study is to quantitatively compare the performance of classical and deep learning-based CT image
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