论文标题
金属使用基于分数的生成模型在CBCT投影中的涂料
Metal Inpainting in CBCT Projections Using Score-based Generative Model
论文作者
论文摘要
在骨科手术期间，通常在移动C臂系统下进行金属植入物或螺钉的插入。由于金属的衰减很大，因此在3D重建中发生了严重的金属伪像，从而大大降低了图像质量。为了减少工件，已经开发了许多金属伪像还原算法，并且在投影域中涂有金属是必不可少的步骤。在这项工作中，基于分数的生成模型在模拟的膝关节投影上进行了训练，并通过在条件重采样过程中删除噪声来获得成分图像。结果暗示，与基于分数的生成模型对图像具有更详细的信息，并且与基于插值和基于CNN的方法相比，达到了最低的平均绝对误差和最高峰值信号到噪声。此外，基于得分的模型还可以用大圆形和矩形面具恢复预测，从而显示其在介入任务中的概括。
During orthopaedic surgery, the inserting of metallic implants or screws are often performed under mobile C-arm systems. Due to the high attenuation of metals, severe metal artifacts occur in 3D reconstructions, which degrade the image quality greatly. To reduce the artifacts, many metal artifact reduction algorithms have been developed and metal inpainting in projection domain is an essential step. In this work, a score-based generative model is trained on simulated knee projections and the inpainted image is obtained by removing the noise in conditional resampling process. The result implies that the inpainted images by score-based generative model have more detailed information and achieve the lowest mean absolute error and the highest peak-signal-to-noise-ratio compared with interpolation and CNN based method. Besides, the score-based model can also recover projections with big circlar and rectangular masks, showing its generalization in inpainting task.
