## 4 Experiments and Results
### SPARE Challenge Dataset  
The SPARE challenge dataset is a clinical sparse-view 4D CBCT dataset. We evaluated the proposed method and counterpart methods on the CV_P1\(T\)01 dataset, a subset of the SPARE challenge dataset. To evaluate the performance of reducing the noise in the LDCT scan, we regarded the CT scan with half of the projections as LDCT and the CT scan with all of the projections as NDCT. Using half of the projections to reconstruct the 3D image indicates that half of the dose is irradiated.  
The CV_P1\(T\)01 dataset is a 4D CT dataset. Thus, it can be divided into 10 subdatasets, depending on the respiratory phase. Therefore, all image data in the SPARE dataset have a respiratory phase value from 1 to 10. We used image data with a respiratory phase value of 1 as the testing data, and the rest of the data with respiratory phase values of 2-9 were used as training data. We built the proposed models in two types: \(DPS^{s}\) and \(DPS^{d}\). \(DPS^{s}\) was trained using half of the projection data with a respiratory phase value of one and was tested on the same dataset used in the training phase to demonstrate that it could be trained efficiently on a small dataset, like single-image denoising, as the usual practice in self-supervised denoising methods [24; 26]. \(DPS^{d}\) and other deep learning-based methods (\(N2V\) and \(N2S\)) were trained using 2039 projection images with a size of \(1024\times 768\) (images with response phase values of 2-9).  
The method for evaluating the performance of each approach was as follows. For the counterpart methods, the 3D data used for the performance evaluation were reconstructed by the FBP-based reconstruction algorithm with half of the denoised projection images, with a respiratory phase value of one. For the proposed method, the 3D data used for the performance evaluation were generated by the method described in Section 3 with half of the projection images, with a respiratory phase value of one. The 3D images generated in these ways were compared to the 3D images reconstructed with all projection images, with a respiratory phase value of one. The size of the reconstructed 3D images was \(448\times 160\times 448\).  
Table 1 and Figure 4 present the performance comparison. Table 1 lists the quantitative results of various denoising methods, including the proposed method. In Table 1, single image set learning or non-learning methods means the deep learning-based denoising methods that were trained with a single image set or conventional image processing-based denoising methods. Dataset-based deep learning method refers to the deep learning-based denoising methods that were trained with multiple sets of images.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Single Image Set Learning or Non-Learning Methods**} & \multicolumn{2}{c}{**Dataset-Based Deep Learning Method**} \\ \hline  & _NLM_ & _TV_ & _DPS\({}^{s}\)_ & _N2S_ & _N2V_ & _DPS\({}^{d}\)_ \\ \hline PSNR (dB) & 24.06 & 26.21 & **30.680** & 23.97 & 25.83 & **30.667** \\ SSIM & 0.55 & 0.65 & **0.787** & 0.56 & 0.67 & **0.786** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative results from the SPARE challenge dataset.  
The method performed better than the counterparts (i.e., non-learning- and learning-based methods). Moreover, the proposed method could train a denoising neural network efficiently, even on a small dataset, as \(DPS^{s}\) had comparable results to \(DPS^{d}\). Figure 4 depicts the visual comparison of various denoising methods. The microstructures were blurred, and some artifacts occurred in the results of the counterpart methods. However, the results of the proposed method exhibited very comparable quality with the target images. Therefore, the proposed method had promising results both visually and quantitatively.