Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications
| العنوان: | Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications |
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| المؤلفون: | Leuschner, Johannes, Schmidt, Maximilian, Ganguly, Poulami Somanya, Andriiashen, Vladyslav, Coban, Sophia Bethany, Denker, Alexander, Bauer, Dominik, Hadjifaradji, Amir, Batenburg, Kees Joost, Maass, Peter, Van Eijnatten, Maureen |
| المساهمون: | Medical Image Analysis, EAISI Health |
| المصدر: | Journal of Imaging, Vol 7, Iss 44, p 44 (2021) Journal of Imaging Volume 7 Issue 3 Journal of Imaging, 7(3):44. Multidisciplinary Digital Publishing Institute (MDPI) Journal of Imaging, 7, 44.1-44.49 |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | low-dose, sparse-angle, quantitative comparison, deep learning, lcsh:R858-859.7, lcsh:Photography, lcsh:Electronic computers. Computer science, computed tomography (CT), image reconstruction, lcsh:TR1-1050, lcsh:Computer applications to medicine. Medical informatics, Article, lcsh:QA75.5-76.95 |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2313-433X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::735ad229385ba6689a2b602315ea7093 https://www.mdpi.com/2313-433X/7/3/44 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....735ad229385ba6689a2b602315ea7093 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 2313433X |
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