RA-CMF: Region-Adaptive Conditional MeanFlow for CT Image Reconstruction
For medical imaging practitioners, this method addresses the challenge of inconsistent CT image quality across different scanners and protocols by adaptively enhancing difficult regions while stabilizing already sufficient areas.
The paper proposes a region-adaptive conditional MeanFlow pipeline for CT image reconstruction that combines conditional flow-based enhancement with reinforcement learning-driven spatial control, achieving high accuracy in tumor ROI with average radiomic feature CCC of 0.96, PSNR of 31.30, and SSIM of 0.94, while improving overall image quality to PSNR of 34.23 and SSIM of 0.95.
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 enhancement trajectory by predicting image-conditioned flow fields given intermediate image states. The image enhancement network is trained with a MeanFlow consistency loss along with the image reconstruction loss. In order to provide an adaptive refinement process in terms of spatial location of enhancements, 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 enhancement processes. Our policy network is trained through reinforcement learning in a policy gradient framework, where the goal of the training reward is to maximize improvement of enhancements while minimizing unnecessary computations and avoiding instabilities. In this way, our approach combines conditional flow-based enhancement with reinforcement learning-based spatial enhancement control. This allows our approach to focus more attention on enhancing difficult areas while stabilizing areas already showing sufficient quality. Our results show high accuracy in the tumor ROI, with the average radiomic feature CCC being 0.96, an average PSNR of 31.30 $\pm$ 4.16, and average SSIM of 0.94 $\pm$ 0.07. 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.