Unsupervised learning for inverse problems in computed tomography

Physics > Medical Physics [Submitted on 7 Aug 2025 (v1), last revised 11 Aug 2025 (this version, v2)] Unsupervised learning for inverse problems in computed tomography Laura Hellwege, Johann Christopher Engster, Moritz Schaar, Thorsten M. Buzug, Maik Stille This study presents an unsupervised deep learning approach for computed tomography (CT) image reconstruction, leveraging the inherent similarities between deep neural network training and conventional iterative reconstruction methods. By incorporating forward and backward projection layers within the deep learning framework, we demonstrate the feasibility of reconstructing images from projection data without relying on ground-truth images. Our method is evaluated on the two-dimensional 2DeteCT dataset, showcasing superior performance in terms of mean squared error (MSE) and structural similarity index (SSIM) compared to traditional filtered backprojection (FBP) and maximum likelihood (ML) reconstruction techniques. Additionally, our approach significantly reduces reconstruction time, making it a promising alternative for real-time medical imaging applications. Future work will focus on extending this methodology to three-dimensional reconstructions and enhancing the adaptability of the projection geometry. Comments: 13 pages, 9 Figures Subjects: Medical Physics (physics.med-ph); Artificial Intelligence (cs.AI) Cite as: arXiv:2508.05321 [physics.med-ph]   (or arXiv:2508.05321v2 [physics.med-ph] for this version)   https://doi.org/10.48550/arXiv.2508.05321 Focus to learn more Submission history From: Laura Hellwege [view email] [v1] Thu, 7 Aug 2025 12:25:48 UTC (3,394 KB) [v2] Mon, 11 Aug 2025 11:25:37 UTC (3,394 KB) Access Paper: HTML (experimental) view license Current browse context: physics.med-ph < prev   |   next > new | recent | 2025-08 Change to browse by: cs cs.AI physics References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)