用深度学习同时去金属伪影并转换CT成高能影像，减少患者辐射。
ReMAR-DS: Recalibrated Feature Learning for Metal Artifact Reduction and CT Domain Transformation
- 通过增强特征重校准的编码器-解码器结构，聚焦关键区域与通道特征。
- 在真实数据上实现高质量MVCT-like重建，有效抑制伪影并保留解剖结构。
- 适合放疗规划场景，可减少患者重复接受高剂量扫描。
kVCT成像中的伪影会降低图像质量，影响临床决策。本文提出一种深度学习框架ReMAR-DS，用于金属伪影去除（MAR）及从kVCT到MVCT的域转换。该框架采用带增强特征重校准的编码器-解码器结构，在重建过程中仅利用相关信息，有效降低伪影并保持解剖结构完整性。通过注入编码器块的重校准特征，模型聚焦于含伪影的空间区域，并突出跨通道的关键特征，从而提升伪影区域的重建效果。与传统MAR方法不同，该方法弥合了高分辨率kVCT与抗伪影的MVCT之间的差距，提升了放疗规划质量。实验通过定性与定量评估验证了其生成高质量MVCT-like影像的能力。临床上，可使肿瘤科医生仅依赖kVCT，避免重复进行高剂量MVCT扫描，显著降低癌症患者的辐射暴露。
原文摘要 · Abstract (English)
Artifacts in kilo-Voltage CT (kVCT) imaging degrade image quality, impacting clinical decisions. We propose a deep learning framework for metal artifact reduction (MAR) and domain transformation from kVCT to Mega-Voltage CT (MVCT). The proposed framework, ReMAR-DS, utilizes an encoder-decoder architecture with enhanced feature recalibration, effectively reducing artifacts while preserving anatomical structures. This ensures that only relevant information is utilized in the reconstruction process. By infusing recalibrated features from the encoder block, the model focuses on relevant spatial regions (e.g., areas with artifacts) and highlights key features across channels (e.g., anatomical structures), leading to improved reconstruction of artifact-corrupted regions. Unlike traditional MAR methods, our approach bridges the gap between high-resolution kVCT and artifact-resistant MVCT, enhancing radiotherapy planning. It produces high-quality MVCT-like reconstructions, validated through qualitative and quantitative evaluations. Clinically, this enables oncologists to rely on kVCT alone, reducing repeated high-dose MVCT scans and lowering radiation exposure for cancer patients.
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