CARE：通过双潜在引导实现无需训练的医学图像可控修复
CARE: Training-Free Controllable Restoration for Medical Images via Dual-Latent Steering
AI总结：
本文提出CARE框架，通过双潜在策略在推理过程中平衡结构保持与先验引导，实现无需训练的可控医学图像修复，提升修复质量并减少不合理的重建风险。
摘要：
医学图像修复对于提高嘈杂、不完整和有伪影的临床扫描的可用性至关重要，但现有方法通常依赖于任务特定的重新训练，且在忠实重建与先验驱动增强之间的权衡控制有限。本工作提出CARE，一种无需训练的可控修复框架，通过双潜在策略在推理过程中显式平衡结构保持和先验引导细化。CARE采用双潜在修复策略，其中一条分支强制数据保真度和解剖一致性，另一条分支利用生成先验来恢复缺失或退化的信息。一个风险感知的自适应控制器根据修复不确定性和局部结构可靠性动态调整各分支的贡献，使在不增加模型训练的情况下实现保守或增强导向的修复模式。我们在嘈杂和不完整的医学成像场景中评估了CARE，并显示其在保持临床相关结构的同时实现了强大的修复质量，并减少了不合理的重建风险。所提出的方法为更安全、可控和部署友好的医学图像修复提供了实用的一步。
英文摘要：
Medical image restoration is essential for improving the usability of noisy, incomplete, and artifact-corrupted clinical scans, yet existing methods often rely on task-specific retraining and offer limited control over the trade-off between faithful reconstruction and prior-driven enhancement. This lack of controllability is especially problematic in clinical settings, where overly aggressive restoration may introduce hallucinated details or alter diagnostically important structures. In this work, we propose CARE, a training-free controllable restoration framework for real-world medical images that explicitly balances structure preservation and prior-guided refinement during inference. CARE uses a dual-latent restoration strategy, in which one branch enforces data fidelity and anatomical consistency while the other leverages a generative prior to recover missing or degraded information. A risk-aware adaptive controller dynamically adjusts the contribution of each branch based on restoration uncertainty and local structural reliability, enabling conservative or enhancement-focused restoration modes without additional model training. We evaluate CARE on noisy and incomplete medical imaging scenarios and show that it achieves strong restoration quality while better preserving clinically relevant structures and reducing the risk of implausible reconstructions and show that it achieves strong restoration quality while better preserving clinically relevant structures and reducing the risk of implausible reconstructions. The proposed approach offers a practical step toward safer, more controllable, and more deployment-ready medical image restoration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
