# Sparse-View Artifact Correction of High-Pixel-Number Synchrotron Radiation CT
## 2 Methods and Data
### Data Preparation and Image Quality Assessment  
Synchrotron radiation CT is an ideal method for high-resolution 3D non-destructive imaging of mouse brains, and the radiation damage can be further reduced by the sparse-view CT method. We used alcoholic dehydration mouse brains under 12 keV monochromatic light generated by a double crystal monochromator at the X-ray Imaging and Biomedical Application Beamline Station (BL13W1) of Shanghai Synchrotron Radiation Facility (SSRF). The distance between the sample and the detector was 20 cm, and the pixel size of the detector was 6.5 \(\upmu\)m \(\times\) 6.5 \(\upmu\)m. Meanwhile, we used a 2\(\times\) objective lens, so each pixel of the CT slice was 3.25 \(\upmu\)m. In total, 1200 projections were collected at 180 angles, and their reconstruction slices were used as labels for network training. In total, 400 views (or 300 views, 200 views, 100 views) were sampled at uniform intervals from all projections, and the reconstructed slices were used as input to the network. The Artifacts are shown in Figure 1. Sparse sampling results in the reconstructed slice contain aliasing artifacts. It should be noted that the slice also contains ring artifacts. However, our focus is on the correction of aliasing artifacts. Therefore, ring artifacts were not corrected prior to training but were considered as part of the sample structure. Furthermore, we used five mouse brain samples, of which three (2708 slices) were used for training, one was used as the validation dataset (474 slices), and one (865 slices) was used as the test set. It is worth noting that since the resolution of the image is 3.25 \(\upmu\)m, the thickness of each layer in the reconstructed CT slices is also 3.25 \(\upmu\)m. This implies that the feature differences between neighboring slices are very small, resulting in insufficiently significant feature differences in the dataset. Such characteristics of synchrotron CT data pose a challenge for deep learning training.  
There are two ways to evaluate image quality: qualitative and quantitative. The performance of the proposed method is qualitatively evaluated by observing the differences in features between the images after artifact correction and the reference (label) images. For quantitative evaluation, there are full-reference and no-reference metrics [33]. Given the availability of label images, we utilize full-reference metrics (PSNR and MS-SSIM [34]) to evaluate the performance of the proposed artifact correction method.The larger the peak signal-to-noise ratio, the better the quality of the network output image. The MS-SSIM index performs down-sampling M (M takes 5) times on the image to represent the similarity of the network output image and the label at different scales. The closer the value is to 1, the closer the network output image and the label are. Its expression is as follows:  
\[PSNR(Y^{\prime},Y)=10 log_{10}\frac{MAX^{2}}{MSE(Y^{\prime},Y)} \tag{8}\]  
\[MS-SSIM=\left[l_{M}(Y^{\prime},Y)\right]^{\alpha_{M}}\prod_{j=1}^{M}c_{j}(Y^{ \prime},Y)^{\beta_{j}}s_{j}(Y^{\prime},Y)^{\gamma_{j}} \tag{9}\]  
MAX represents the maximum value of the true (label) image. \(\alpha\),\(\beta\),\(\gamma\) are weight parameters used to adjust the relative importance of different components luminance \(l\), contrast \(c\) and structure \(s\). Among these, \(\beta_{1}=\gamma_{1}=0.0448\), \(\beta_{2}=\gamma_{2}=0.2856\), \(\beta_{3}=\gamma_{3}=0.3001\), \(\beta_{4}=\gamma_{4}=0.2363\), \(\alpha_{5}=\beta_{5}=\gamma_{5}=0.1333\) and \(l,c,s\) corresponds to Equations (2)-(4) in the previous paragraph.