RA-CMF: Region-Adaptive Conditional MeanFlow for CT Image Reconstruction

[2605.00901] RA-CMF: Region-Adaptive Conditional MeanFlow for CT Image Reconstruction Skip to main content Search Submit Donate Log in Computer Science > Computer Vision and Pattern Recognition arXiv:2605.00901 (cs) [Submitted on 28 Apr 2026 ( v1 ), last revised 25 Aug 2026 (this version, v2)] Title: RA-CMF: Region-Adaptive Conditional MeanFlow for CT Image Reconstruction Authors: Md Shifatul Ahsan Apurba , Md Selim , Jin Chen View PDF HTML (experimental) Abstract: The use of CT imaging is important for screening, diagnosis, therapy planning, and prognosis of lung cancers. Unfortunately, due to differences in imaging protocols and scanner models, CT images acquired by different means may show large differences in noise statistics, contrast, and texture. In this study, we develop a novel conditional MeanFlow pipeline for CT image reconstruction. We introduce a conditional MeanFlow network that models the reconstruction trajectory by predicting image-conditioned flow fields given intermediate image states. The image reconstruction network is trained with a MeanFlow consistency loss along with the image reconstruction loss. In order to provide a spatially adaptive refinement process, we integrate a regional reinforcement learning-driven policy network into our approach. The policy network receives information about the MeanFlow rollouts and provides predictions in terms of tile-wise refinement budgets, stopping criteria, and total budget allocation of refinement processes. Our policy network is trained through reinforcement learning in a policy gradient framework, where the goal of the training reward is to maximize reconstruction quality while minimizing unnecessary computations and avoiding instabilities. In this way, our approach combines conditional flow-based reconstruction with reinforcement learning-based spatial reconstruction control. Our results show high accuracy in the tumor ROI, with the average radiomic feature CCC being $0.93 \pm 0.09$, an average PSNR of $31.94 \pm 2.64$, and average SSIM of $0.97 \pm 0.03$. Moreover, there is an improvement in the overall quality of images, with an average PSNR of $34.23 \pm 1.71$ and average SSIM of $0.95 \pm 0.01$. Comments: 17 pages, 5 figures, ICIBM 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2605.00901 [cs.CV] (or arXiv:2605.00901v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2605.00901 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Md Shifatul Ahsan Apurba [ view email ] [v1] Tue, 28 Apr 2026 18:28:05 UTC (20,317 KB) [v2] Tue, 25 Aug 2026 08:27:31 UTC (21,641 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CV < prev | next > new | recent | 2026-05 Change to browse by: cs cs.AI References & Citations SciX 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 Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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