自审计残差漂移模型用于保留病理特征的加速膝关节MRI
Self-Auditing Residual Drifting for Pathology-Preserving Accelerated Knee MRI
AI总结：
提出SA-RDM-DC模型，通过物理条件残差漂移生成和自审计机制，在加速膝关节MRI中实现高保真重建并保留病理特征，优于多种基线方法。
摘要：
加速磁共振成像减少了采集时间，但从欠采样k空间重建可能会模糊诊断相关结构或引入全局图像指标无法捕捉的失败。我们提出了SA-RDM-DC，一种具有数据一致性的自审计残差生成漂移模型，用于加速膝关节MRI。该方法通过训练一个物理条件的漂移场，从零填充重建向全采样残差校正方向适应新提出的生成漂移范式到加速MRI。它预测图像和缺失k空间残差校正，强制与采集的k空间保持数据一致性，使用频率感知和残差漂移监督来恢复细节，并在同一推理过程中生成密集误差图和切片级风险评分。我们在加速因子为4、8和12的多线圈fastMRI膝关节数据上评估SA-RDM-DC，使用fastMRI+病理标注进行区域级和基于分类器的任务保留评估，并在SKM-TEA上进行零样本和微调协议转移评估。与零填充重建、UNet-image-SENSE、DC-UNet、Score-Diffusion、ELF-Diff、SENSE-VarNet和MoDL基线相比，SA-RDM-DC在fastMRI各加速因子下实现了最高的SSIM，同时保持亚秒级每切片推理，避免了迭代扩散基线的长采样时间。在病理感知分析中，SA-RDM-DC保留了病灶区域结构保真度，并减少了半月板预测的不稳定性。其自审计分数强烈识别fastMRI上的高误差重建，并在SKM-TEA协议转移下部分转移作为选择性审查信号。这些结果支持联合考虑图像保真度、病理保留、运行时间和病例特定可靠性的重建评估。
英文摘要：
Accelerated magnetic resonance imaging reduces acquisition time, but reconstruction from undersampled k-space can blur diagnostically relevant structures or introduce failures that are not captured by global image metrics. We propose SA-RDM-DC, a Self-Auditing Residual generative Drifting Model with Data Consistency for accelerated knee MRI. The method adapts the newly proposed generative drifting paradigm to accelerated MRI by training a physics-conditioned drift field from the zero-filled reconstruction toward the fully sampled residual correction. It predicts image- and missing-k-space residual corrections, enforces data consistency with acquired k-space, uses frequency-aware and residual drifting supervision to recover fine detail, and produces dense error maps and slice-level risk scores in the same inference pass. We evaluate SA-RDM-DC on multi-coil fastMRI knee data at acceleration factors of 4, 8, and 12, with fastMRI+ pathology annotations for region-level and classifier-based task preservation, and on SKM-TEA for zero-shot and fine-tuned protocol-shift evaluation. Compared with zero-filled reconstruction, UNet-image-SENSE, DC-UNet, Score-Diffusion, ELF-Diff, SENSE-VarNet, and MoDL baselines, SA-RDM-DC achieves the highest SSIM across fastMRI acceleration factors while retaining subsecond per-slice inference and avoiding the long sampling time of iterative diffusion baselines. In pathology-aware analysis, SA-RDM-DC preserves lesion-region structural fidelity and reduces meniscus prediction instability. Its self-auditing scores strongly identify high-error reconstructions on fastMRI and partially transfer as a selective-review signal under SKM-TEA protocol shift. These results support reconstruction evaluation that jointly considers image fidelity, pathology preservation, runtime, and case-specific reliability.
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。
