Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:2076-2079. doi: 10.1109/EMBC48229.2022.9871947.
We compare image domain and projection domain denoising approaches with self-supervised learning for ultra low-dose cone-beam CT (CBCT), where number of detected x-ray photons is significantly low. For image-domain self-supervised denoising, we first reconstruct CBCT images with the standard filtered backprojection. For model training, we use blind-spot filtering to partially blind images and recover the blind spots. For projection-domain self-supervised denoising, we regard the post-log projections as training examples of convolutional neural network. From experimental results with various low-dose CBCT settings, the projection-domain denoiser outperforms the image-domain denoiser both in image quality and accuracy for ultra low-dose CBCT.
我们比较了图像域和投影域去噪方法与基于自监督学习的超低剂量锥形束 CT（CBCT）去噪方法，在超低剂量 CBCT 中，检测到的 X 射线光子数量明显较低。对于图像域自监督去噪，我们首先使用标准滤波反投影重建 CBCT 图像。对于模型训练，我们使用盲点滤波对图像进行部分盲处理，并恢复盲点。对于投影域自监督去噪，我们将对数后投影视为卷积神经网络的训练示例。通过各种低剂量 CBCT 设置的实验结果，在超低剂量 CBCT 的图像质量和准确性方面，投影域去噪器均优于图像域去噪器。
