AI 中文总结
COBRA2026数据集用于开发和评估放疗CBCT相关方法，含欧洲六个中心867名患者数据，经处理后分为训练、验证和测试集，支持多种研究，以特定许可发布并附代码，构成重建挑战基础。
AI 中文摘要
COBRA2026数据集是一个大规模、多中心的原始放射治疗锥束计算机断层扫描（CBCT）采集资源，用于开发和评估传统及基于学习的重建与图像校正方法。它包含来自欧洲六个中心867名接受盆腔放疗患者的数据，使用Elekta和Varian成像系统采集。数据集包括原始投影数据、采集几何信息、校准和校正信息、临床重建的CBCT图像及相应的计划CT图像。特定供应商文件被匿名化并转换为开放格式。计划CT图像与每日CBCT解剖结构进行变形配准，并使用相应采集几何模拟匹配投影。所有病例都经过视觉质量控制，排除有大量处理或配准错误的病例。约950GB的数据集分为训练集、验证集和测试集，分别包含692、52和123个病例。投影堆栈和体积图像以压缩的MetaImage文件提供，几何和元数据以XML和YAML格式提供。COBRA2026支持全视图和稀疏视图重建、低剂量成像、伪影和散射校正、运动补偿及合成CT生成等研究。该数据集根据CC BY - NC �.0许可发布，在Zenodo上索引（doi：https://doi.org/10.5281/zenodo.21322350），并附有公开可用的预处理和基线重建代码，还构成了COBRA2026重建挑战的基础。
英文摘要
The COBRA2026 dataset is a large-scale, multicenter resource of raw radiotherapy cone-beam computed tomography (CBCT) acquisitions created for the development and evaluation of conventional and learning-based reconstruction and image-correction methods. It contains data from 867 patients undergoing pelvic radiotherapy at six European centers, acquired using Elekta and Varian imaging systems. For each case, the dataset includes raw projection data, acquisition geometry, calibration and correction information, clinically reconstructed CBCT images, and corresponding planning CT images. Vendor-specific files were anonymized and converted into open formats. Planning CT images were deformably registered to the daily CBCT anatomy, and matched projections were simulated using the corresponding acquisition geometry. All cases underwent visual quality control, and cases with substantial processing or registration errors were excluded. The approximately 950 GB dataset is divided into training, validation, and test sets containing 692, 52, and 123 cases, respectively. Projection stacks and volumetric images are provided as compressed MetaImage files, with geometry and metadata supplied in XML and YAML formats. COBRA2026 supports research on full- and sparse-view reconstruction, low-dose imaging, artifact and scatter correction, motion compensation, and synthetic CT generation. The dataset is released under the CC BY-NC 4.0 license, indexed on Zenodo (doi:10.5281/zenodo.21322350), and accompanied by openly available preprocessing and baseline reconstruction code. It also forms the basis of the COBRA2026 reconstruction challenge.
