基于深度学习的乳腺微焦点 CBCT 成像中伪影抑制。

BACKGROUND: Cone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation times. One simple solution to reduce the acquisition scan time is to decrease of the number of projections, but this method generates streak artifacts on breast specimen images. Furthermore, the presence of a metallic-needle marker on a breast specimen causes metal artifacts that are prominently visible in the images. In this work, we propose a deep learning-based approach for suppressing both streak and metal artifacts in CBCT. METHODS: In this work, sinogram datasets acquired from CBCT and a small number of projections containing metal objects were used. The sinogram was first modified by removing metal objects and up sampling in the angular direction. Then, the modified sinogram was initialized by linear interpolation and synthesized by a modified neural network model based on a U-Net structure. To obtain the reconstructed images, the synthesized sinogram was reconstructed using the traditional filtered backprojection (FBP) approach. The remaining residual artifacts on the images were further handled by another neural network model, ResU-Net. The corresponding denoised image was combined with the extracted metal objects in the same data positions to produce the final results. RESULTS: The image quality of the reconstructed images from the proposed method was improved better than the images from the conventional FBP, iterative reconstruction (IR), sinogram with linear interpolation, denoise with ResU-Net, sinogram with U-Net. The proposed method yielded 3.6 times higher contrast-to-noise ratio, 1.3 times higher peak signal-to-noise ratio, and 1.4 times higher structural similarity index (SSIM) than the traditional technique. Soft tissues around the marker on the images showed good improvement, and the mainly severe artifacts on the images were significantly reduced and regulated by the proposed. CONCLUSIONS: Our proposed method performs well reducing streak and metal artifacts in the CBCT reconstructed images, thus improving the overall breast specimen images. This would be beneficial for clinical use.

背景：锥形束计算机断层扫描（CBCT）已被引入用于乳房标本成像，以识别保乳术中异常组织的游离切除边缘。众所周知，典型的微 CT 需要较长的采集和计算时间。减少采集扫描时间的一个简单方法是减少投影数量，但这种方法会在乳房标本图像上产生条纹伪影。此外，乳房标本上的金属针标记会导致金属伪影，这些伪影在图像中非常明显。在这项工作中，我们提出了一种基于深度学习的方法来抑制 CBCT 中的条纹和金属伪影。

方法：在这项工作中，使用了从 CBCT 采集的正弦图数据集和包含金属物体的少量投影。正弦图首先通过去除金属物体和在角度方向上的上采样来修改。然后，通过基于 U-Net 结构的修改后的神经网络模型，用线性插值初始化修改后的正弦图。为了获得重建图像，用传统的滤波反投影（FBP）方法重建合成的正弦图。通过另一个神经网络模型 ResU-Net 进一步处理图像上剩余的残余伪影。将具有相同数据位置的提取的金属物体的合成图像与对应的去噪图像相结合，以产生最终结果。

结果：与传统的 FBP、迭代重建（IR）、线性插值的正弦图、ResU-Net 去噪、U-Net 正弦图相比，所提出的方法重建图像的图像质量得到了更好的改善。与传统技术相比，该方法的对比度噪声比提高了 3.6 倍，峰值信噪比提高了 1.3 倍，结构相似性指数（SSIM）提高了 1.4 倍。图像上标记周围的软组织得到了很好的改善，图像上的主要严重伪影得到了显著减少和调节。

结论：我们提出的方法在 CBCT 重建图像中减少条纹和金属伪影的效果很好，从而提高了整体乳房标本图像的质量。这将有益于临床应用。