AI 中文总结
PredRA提出结合确定性组件提取与受控随机细化的快速医学图像翻译框架，在保持感知质量的同时提高保真度，并以更少参数和采样步数达到或超越更大模型性能。
AI 中文摘要
强配对医学图像翻译包含大量可直接从源图像预测的组件。当前现实是，纯确定性预测会平滑掉精细细节，而生成模型虽能恢复细节，但也可能引入不必要或潜在有害的变异。我们提出PredRA，一个快速框架，将确定性预测作为稳定参考，并在生成细化过程中从残差中进一步提取额外的确定性组件，从而在保持感知质量的同时提高保真度。目标很简单：我们将整个基于残差的生成过程视为一个优化问题，并推导出一个实用解决方案，通过受控细化恢复有用的残差细节，同时使预测接近配对目标。机制研究进一步表明，有用的残差信息遵循结构化模式，但其有用性在体素水平上难以可靠估计。因此，我们全局控制残差细化添加到确定性预测中的量，从而减少不必要不确定性的累积。因此，PredRA将确定性组件提取与受控随机细化相结合，实现快速、保真且感知强的医学图像翻译。我们在多个真实世界数据集上验证了该方法，表明与完全残差细化相比，受控细化提高了对配对目标的保真度，同时保留了生成建模的大部分感知优势。PredRA以1.4-11.9倍更少的总参数和3.1-26.2倍更少的可训练参数，达到与更大规模的最先进模型相当或更优的性能，而其32步流采样器所需的采样步数比匹配的1000步DDPM少31.25倍。
英文摘要
Strongly paired medical image translation contains a substantial component that can be predicted directly from the source. The current reality is that pure deterministic prediction can smooth away fine detail, while generative models can recover detail but may also introduce unnecessary or potentially harmful variation. We propose PredRA, a fast framework that uses the deterministic prediction as a stable reference and further extracts additional deterministic components from the residual during generative refinement, thereby improving fidelity while maintaining perceptual quality. The goal is simple: we view the entire residual-based generative process as an optimization problem and derive a practical solution that recovers useful residual detail through controlled refinement while keeping the prediction close to the paired target. Mechanism studies further show that useful residual information follows structured patterns, but its usefulness is difficult to estimate reliably at the voxel level. We therefore globally control how much of the residual refinement is added to the deterministic prediction, thereby reducing the accumulation of unnecessary uncertainty. PredRA therefore combines deterministic component extraction with controlled stochastic refinement for fast, fidelity-preserving, and perceptually strong medical image translation. We validate the approach across multiple real-world datasets, showing that controlled refinement improves fidelity to the paired target compared with full residual refinement while retaining much of the perceptual benefit of generative modeling. PredRA achieves competitive or superior performance to substantially larger state-of-the-art models with 1.4-11.9x fewer total parameters and 3.1-26.2x fewer trainable parameters, while its 32-step flow sampler requires 31.25x fewer sampling steps than the matched 1000-step DDPM.
CommentsPreprint. 14 pages, 7 figures, 12 tables. Code: https://github.com/jianhai-zhang/RredRA
