# Deep Learning-Based Denoising in Brain Tumor CHO PET: Comparison with Traditional Approaches  
[PERSON]  
1 Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (S.X. ); [EMAIL_ADDRESS] (X.X.) [PERSON]  
1 Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (S.X. ); [EMAIL_ADDRESS] (X.X.) [PERSON]  
3 Department of Nuclear Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; [EMAIL_ADDRESS] (Y.Z.) [PERSON]  
4 Department of Head and Neck Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; [EMAIL_ADDRESS] (Y.Z.) [PERSON]  
5 Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China (Y.Z.) Ziren Kong  
4 Department of Nuclear Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; [EMAIL_ADDRESS] (Y.Z.) Ziren Kong  
4 Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; [EMAIL_ADDRESS] (Y.Z  
the metabolic tumor. However, the images remain noisy due to the limited number of photons captured during the PET scan, and higher counts of detected photons rely on a higher tracer dose and longer scanning time, which may give rise to the potential ionizing radiation damage to patients and medical staff or decrease the examination efficiency due to the long scanning duration. Therefore, developing a denoising method for CHO PET images of brain tumors that can produce a clearer image and retain the tumor property, would facilitate the clinical interpretation of PET data and facilitate the diagnosis of brain tumors. Some traditional denoising methods have been applied for PET image noise reduction. Clinically, the Gaussian filter is a basic and simple approach, which convolves the reconstructed image with a Gaussian kernel. However, the Gaussian filter smooths out noise and detailed structures at the same time so this method is not edge-preserving. Multiple conventional algorithms, including bilateral filter [1], non-local means (NLM) [2], wavelet filter [3], block-matching and 3D filtering (BM3D) [4] and image-guided filter, have been developed to reduce the PET image noise while trying to preserve the details, but their clinical utility remains to be improved. Over the past several years, deep learning techniques in image processing have provided novel approaches to acquiring high-quality images in the field of nuclear medicine imaging. Supervised networks require paired datasets for input and target, and have been proposed for PET image noise reduction, employing the high-quality images to restore the noisy images [5; 6; 7]. However, the feasibility to acquire clean PET data is limited in clinical practice, and it takes additional works to obtain paired datasets which is a burden for clinical work. Therefore, unsupervised and self-supervised learning techniques, which can train the models without labels, have been applied to PET imaging. Deep image prior (DIP) [8], which employs random noise and a corrupted image as the input and target without pre-training, shows the CNN's intrinsic ability of learning structure information from a single corrupted image, and has been developed for FDG PET denoising with the patient's prior image as the input [9; 10; 11]. Meanwhile, networks trained by large datasets have been reported to be useful for improving PET image quality without clean data, such as Noise2 Noise (N2N) and Noise2 Void (N2V).