{"title":"U-Net Transfer Learning for Image Restoration on Sparse CT Reconstruction in Pre-Clinical Research","authors":"Huanyi Zhou, Honggang Zhao, Wenlu Wang","doi":"10.1109/ICDMW58026.2022.00053","DOIUrl":null,"url":null,"abstract":"Sparse computed tomography (CT) reconstruction can lead to significant streak artifacts. Image restoration that removes these artifacts while recovering image features is an important area of research in low-dose sparse CT imaging. In pre-clinical research, where a lag still exists in the use of professional CT equipment, existing imaging devices provide limited X-ray dose energy accompanied by strong noise patterns when scanning. Reconstructed CT images contain significant noise and artifacts. We propose a deep transfer learning (DTL) neural network training method that exploits open-source data for initial training and a small-scale detected phantom image with its total variation result for transfer learning to address this issue. We hypothesize that a pre-trained neural network from open-source data has no prior knowledge of our device configuration, which prevents its application on our measured data, and deep transfer learning on small-scale detected phantom can feed specific configurations into the model. Our experiment has demonstrated that our proposed method, incorporating a modified total variation (TV) algorithm, can successfully realize a good balance between artifact removal and image feature restoration.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW58026.2022.00053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Sparse computed tomography (CT) reconstruction can lead to significant streak artifacts. Image restoration that removes these artifacts while recovering image features is an important area of research in low-dose sparse CT imaging. In pre-clinical research, where a lag still exists in the use of professional CT equipment, existing imaging devices provide limited X-ray dose energy accompanied by strong noise patterns when scanning. Reconstructed CT images contain significant noise and artifacts. We propose a deep transfer learning (DTL) neural network training method that exploits open-source data for initial training and a small-scale detected phantom image with its total variation result for transfer learning to address this issue. We hypothesize that a pre-trained neural network from open-source data has no prior knowledge of our device configuration, which prevents its application on our measured data, and deep transfer learning on small-scale detected phantom can feed specific configurations into the model. Our experiment has demonstrated that our proposed method, incorporating a modified total variation (TV) algorithm, can successfully realize a good balance between artifact removal and image feature restoration.