X-ray nanotomography has become an important analysis tool in a wide range of fields. However, the imaging quality is often affected by drift from focal spot movement and mechanical instability. An improved horizontal drift correction method for X-ray nanotomography based on trajectory of sinogram centroid (TSC) is proposed. This method requires neither auxiliary marks nor additional projections. A sliding window TSC fitting method is utilized. The sum of the squared errors (SSE) is calculated between the trajectory and standard sinusoidal curve. The one corresponding to the minimum SSE is chosen to obtain the horizontal drift from the original TSC, which is then used to align the projections. The proposed method is evaluated by both simulation results of the Shepp-Logan phantom and nanotomographic results of the honeybee mouthpart. The results show that this method can quickly and effectively correct the projection horizontal drift.
Low dose computed tomography (LDCT) has attracted considerable interest in medical imaging fields. Reducing tube current intensity and decrease the exposure time are the two main ways in clinic applications. Nevertheless, the resulting statistical noise will seriously degrade CT image quality for diagnosis. To make full use of the original projection data as well as further improving the small dataset processing ability of U-net, this study aimed to investigate a low dose X-ray CT image denoising method via U-net in projection domain (PDI U-net). Meanwhile, in view of avoiding the excessive smoothing of the small structures, the inception module is introduced in the encoding stage of the network. And different convolution kernel operations of 1×1, 3×3, and 5×5 are used in parallel to obtain multi-scale image features, increasing the depth and width of the network while reducing the parameters. Furthermore, the shortcut connection is utilized to transfer the low-level area local detail information to the high-level area. By merging it with the global information of the high-level area, the proposed network can maintain the image details while de-noising. The experimental results show that the method proposed can significantly improve the image quality with clear feature edges and close visual appearance to the reference high dose CT images. Compared with LDCT and Residual Encoder-Decoder Convolutional Neural Network (RED-CNN), the peak signal to noise radio (PSNR) is improved by 9.02dB and 2.74dB, and the structural similarity (SSIM) is improved by 0.43 and 0.07, respectively.
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