{"title":"用于 CT 图像去噪和重建的自监督学习:综述。","authors":"Kihwan Choi","doi":"10.1007/s13534-024-00424-w","DOIUrl":null,"url":null,"abstract":"<p><p>This article reviews the self-supervised learning methods for CT image denoising and reconstruction. Currently, deep learning has become a dominant tool in medical imaging as well as computer vision. In particular, self-supervised learning approaches have attracted great attention as a technique for learning CT images without clean/noisy references. After briefly reviewing the fundamentals of CT image denoising and reconstruction, we examine the progress of deep learning in CT image denoising and reconstruction. Finally, we focus on the theoretical and methodological evolution of self-supervised learning for image denoising and reconstruction.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"14 6","pages":"1207-1220"},"PeriodicalIF":3.2000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11502646/pdf/","citationCount":"0","resultStr":"{\"title\":\"Self-supervised learning for CT image denoising and reconstruction: a review.\",\"authors\":\"Kihwan Choi\",\"doi\":\"10.1007/s13534-024-00424-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This article reviews the self-supervised learning methods for CT image denoising and reconstruction. Currently, deep learning has become a dominant tool in medical imaging as well as computer vision. In particular, self-supervised learning approaches have attracted great attention as a technique for learning CT images without clean/noisy references. After briefly reviewing the fundamentals of CT image denoising and reconstruction, we examine the progress of deep learning in CT image denoising and reconstruction. Finally, we focus on the theoretical and methodological evolution of self-supervised learning for image denoising and reconstruction.</p>\",\"PeriodicalId\":46898,\"journal\":{\"name\":\"Biomedical Engineering Letters\",\"volume\":\"14 6\",\"pages\":\"1207-1220\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11502646/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Engineering Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s13534-024-00424-w\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/11/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Engineering Letters","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s13534-024-00424-w","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Self-supervised learning for CT image denoising and reconstruction: a review.
This article reviews the self-supervised learning methods for CT image denoising and reconstruction. Currently, deep learning has become a dominant tool in medical imaging as well as computer vision. In particular, self-supervised learning approaches have attracted great attention as a technique for learning CT images without clean/noisy references. After briefly reviewing the fundamentals of CT image denoising and reconstruction, we examine the progress of deep learning in CT image denoising and reconstruction. Finally, we focus on the theoretical and methodological evolution of self-supervised learning for image denoising and reconstruction.
期刊介绍:
Biomedical Engineering Letters (BMEL) aims to present the innovative experimental science and technological development in the biomedical field as well as clinical application of new development. The article must contain original biomedical engineering content, defined as development, theoretical analysis, and evaluation/validation of a new technique. BMEL publishes the following types of papers: original articles, review articles, editorials, and letters to the editor. All the papers are reviewed in single-blind fashion.