Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality. Issue 2 (March 2020)

• Record Type: Journal Article
• Authors: Nakamura, Yuko; Higaki, Toru; Tatsugami, Fuminari; Honda, Yukiko; Narita, Keigo; Akagi, Motonori; Awai, Kazuo
• Is Part Of: Journal of computer assisted tomography. Volume 44:Issue 2(2020)
• Publication Date: 2020-03
• Subjects: neural networks (computer) -- tomography, x-ray computed -- machine learning -- artificial intelligence, deep learning; Tomography -- Periodicals; Tomography -- Periodicals; Tomography; Periodicals; 616.0757
• DOI: 10.1097/RCT.0000000000000928
• Languages: English
• ISSNs: 0363-8715

Abstract

Abstract : Abstract: Deep learning (DL), part of a broader family of machine learning methods, is based on learning data representations rather than task-specific algorithms. Deep learning can be used to improve the image quality of clinical scans with image noise reduction. We review the ability of DL to reduce the image noise, present the advantages and disadvantages of computed tomography image reconstruction, and examine the potential value of new DL-based computed tomography image reconstruction. Abstract : Supplemental digital content is available in the text.