aiqbench analysis
This research introduces a novel conditional MeanFlow pipeline enhanced by regional reinforcement learning to optimize CT image reconstruction across varying scanner protocols. By dynamically allocating refinement budgets based on spatial needs, the method significantly improves tumor detection accuracy and image quality metrics like PSNR and SSIM while reducing computational waste. The work represents a meaningful advancement in applying deep generative models and RL agents to solve specific challenges in medical imaging data fidelity.
RA-CMF: Region-Adaptive Conditional MeanFlow for CT Image Reconstruction
arXiv:2605.00901v2 Announce Type: replace-cross 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 $0.95 \pm 0.01$.
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 $0.95 \pm 0.01$.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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
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.
Full text under the publisher's open licence · 345 words
Related coverage
The Machines that Make the Machines
NVIDIA · Oct 7, 2026
ORBIT-2 based Weather and Climate Downscaling and Downscaled Global Forecasts on AMD Instinct
AMD · Jun 8, 2026
TraceCLIP: Recovering Local Semantics from Patch-to-CLS Contributions
arXiv · Jul 31, 2026
Cloud Forensics and the Digital Crime Scene: How Investigation, Prevention, and Resilience Have Evolved
AppDirect · May 21, 2026