# Deep Learning-Based Denoising in Brain Tumor CHO PET: Comparison with Traditional Approaches
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Correlation** & **MTV** & **SUV Max** & **SUV Mean** & **TLA** \\ \hline \multirow{2}{*}{N2N} & Improvement ratio & \(-0.032\) (\(p\) = 0.660) & \(-0.071\) (\(p\) = 0.323) & \(-0.073\) (\(p\) = 0.311) & \(-0.058\) (\(p\) = 0.415) \\  & CNR & 0.431 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) & 0.425 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) & 0.461 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) & 0.449 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) \\ \hline \multirow{2}{*}{N2V} & Improvement ratio & \(-0.031\) (\(p\) = 0.666) & \(-0.066\) (\(p\) = 0.355) & \(-0.066\) (\(p\) = 0.355) & \(-0.058\) (\(p\) = 0.421) \\  & CNR & 0.443 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) & 0.453 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) & 0.498 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) & 0.473 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) \\ \hline \multirow{2}{*}{NLM} & Improvement ratio & \(-0.110\) (\(p\) = 0.126) & \(-0.107\) (\(p\) = 0.134) & \(-0.111\) (\(p\) = 0.120) & \(-0.103\) (\(p\) = 0.151) \\  & CNR & 0.441 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) & 0.456 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) & 0.490 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) & 0.468 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) \\ \hline \multirow{2}{*}{BM3D} & Improvement ratio & \(-0.036\) (\(p\) = 0.618) & \(-0.074\) (\(p\) = 0.302) & \(-0.071\) (\(p\) = 0.325) & \(-0.059\) (\(p\) = 0.407) \\  & CNR & 0.441 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) & 0.451 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) & 0.488 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) & 0.470 (\(p\) \(<\) 1 \(\times\) 10\({}^{-5}\)) \\ \hline \hline \end{tabular}  
* Notes: The values of tumor features change ratios were calculated with outliers that differed from the median by more than three scaled median absolute deviations detected and removed.  
\end{table} 
Table 5: Correlation of CNR and CNR improvement ratio with MTV, SUVmax, SUVmean and TLA. with the generalization to CHO PET images, which have different characteristics compared to FDG. A variety of neural networks have been designed to produce denoised images, and the majority of them are trained by paired data such as short-scan-time and long-scan-time data. Although these approaches are expected to have better performance on the denoising result, the limitation of these methods still narrows their practical application. Supervised denoising networks rely on the experiments bringing out long-scan-time data or down-sampled data to generate paired datasets. However, the short-scan-time data or down-sampled data are not always accessible for the retrospective dataset which are post-processed by Gaussian filters. Hence, the techniques that require ground-truth data are difficult to be adopted in clinical practice. In contrast, N2N and N2V denoising networks are self-supervised/unsupervised learning approaches, and they have the potential capability of clinical generalization. Our denoising algorithm was developed from the finally outputted DICOM data, which is different from some of the previous studies that denoise as one of the post-process steps. Processing the final DICOM data provides clearer images with important tumor features preserved, and it gives us opportunities to deal with the abundant retrospective (original-existed) data. In addition to CHO PET, amino acid PET was also recognized for its low background activity and high tumor-to-normal contrast in brain tumors, and our denoising method may also be utilized for amino acid PET to produce a clearer image. Entropy is considered to represent the variance of an image, with a higher value indicating a higher image disorder of the same subject. For N2N network, which tend to learn the low-frequency signal to fulfill the accuracy of norm regression, the output of this network presented low entropy. The N2V approach is based on the assumption that the noise of images is pixel-wise independent, so that it can filter out the unpredictable noise with the structured noise left.