基于双域多阶段联合降噪的低剂量CT图像重建

Low-dose CT Image Reconstruction Based on Dual-domain Multi-stage Combined Denoising

针对计算机断层成像中辐射剂量的暴露具有癌症诱发的潜在危险,以及单一数据在重建结果中易产生噪声残留、结构过度平滑等问题,提出基于两种不同数据域的低剂量CT图像多阶段联合降噪模型。在生成器第一阶段对低剂量投影数据采用残差U-Net模型进行正弦图恢复,在编解码过程通过嵌入跳跃连接为上采样增加多尺度信息,加快训练收敛速度。对去噪后投影图像用滤波反投影实现频域到空间域转换。在第二阶段利用多尺度卷积对CT重建图像再次去噪,丰富卷积多样性,提高重构精度。此外引入VGG网络捕获不同剂量图像间的感知差异,提高网络表征能力。实验结果表明,该方法获得了较高PSNR,相较于单一域变换,更能有效地利用投影数据与图像数据的互补效应来抑制噪声和伪影,提高重构效果。

Aiming at the exposure to radiation dosage in CT imaging possessing cancer-inducing potential, and the use of a single data resulting in the residual noise and excessive smoothness of the structure in reconstruction results, a multi-stage joint denoising model for low-dose CT images based on two different data domains is proposed. In the first stage of the generator, the residual U-net model was used to recover the low-dose projection data. and the multi-scale information was added to the up-sampling by embedding skip connections in the coding and decoding process to accelerate the training convergence speed. After noise removal, the projection data was switched from frequency space to spatial domain by means of filtering back projection. In the second stage, multi-scale convolution was used to denoise CT reconstructed images again to enrich the convolution diversity and improve the reconstruction accuracy. VGG network was introduced to capture the perceptual difference between CT images of different doses, and the ability of network representation was enhanced. Experimental results show that the proposed method has a higher PSNR, and compared with the single domain transformation, it can effectively use the complementary effect of the projection data and image data to suppress noise and artifact, and improve the reconstruction effect.