用深度学习修复CBCT图像缺失并生成高精度CT，提升放疗定位准确度。
ARTInp: CBCT-to-CT Image Inpainting and Image Translation in Radiotherapy
- 双网络结构：先补全图像缺口，再生成高质量合成CT。
- 在18例患者测试中显著改善图像质量与解剖完整性。
- 适合放疗医生、医学物理师用于复杂治疗的精准剂量规划。
自适应放疗（ART）流程中，需在治疗时评估患者解剖结构以确保剂量精准。锥形束计算机断层扫描（CBCT）因成本低、易集成被广泛使用，但其分辨率低、伪影多，且在全身照射如全骨髓和淋巴结照射（TMLI）中常出现图像不连续，遗漏关键解剖信息。为此，我们提出ARTInp框架，结合图像补全与CBCT到CT的图像转换。该方法采用双网络设计：完成网络填补CBCT体积中的解剖空缺，定制生成对抗网络（GAN）生成高质量合成CT（sCT）。模型基于SynthRad 2023挑战赛的配对CBCT-CT数据集训练，在18名患者的测试集上展现出显著性能提升，具备增强基于CBCT的放疗流程潜力。
原文摘要 · Abstract (English)
A key step in Adaptive Radiation Therapy (ART) workflows is the evaluation of the patient's anatomy at treatment time to ensure the accuracy of the delivery. To this end, Cone Beam Computerized Tomography (CBCT) is widely used being cost-effective and easy to integrate into the treatment process. Nonetheless, CBCT images have lower resolution and more artifacts than CT scans, making them less reliable for precise treatment validation. Moreover, in complex treatments such as Total Marrow and Lymph Node Irradiation (TMLI), where full-body visualization of the patient is critical for accurate dose delivery, the CBCT images are often discontinuous, leaving gaps that could contain relevant anatomical information. To address these limitations, we propose ARTInp (Adaptive Radiation Therapy Inpainting), a novel deep-learning framework combining image inpainting and CBCT-to-CT translation. ARTInp employs a dual-network approach: a completion network that fills anatomical gaps in CBCT volumes and a custom Generative Adversarial Network (GAN) to generate high-quality synthetic CT (sCT) images. We trained ARTInp on a dataset of paired CBCT and CT images from the SynthRad 2023 challenge, and the performance achieved on a test set of 18 patients demonstrates its potential for enhancing CBCT-based workflows in radiotherapy.
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