Journal
RADIOLOGY-ARTIFICIAL INTELLIGENCE
Volume 3, Issue 3, Pages -Publisher
RADIOLOGICAL SOC NORTH AMERICA (RSNA)
DOI: 10.1148/ryai.2021200157
Keywords
Adults and Pediatrics; Computer Aided Diagnosis (CAD); Computer Applications-General (Informatics); Skeletal-Appendicular; Skeletal-Axial; Soft Tissues/Skin
Funding
- National Research Foundation (NRF) - Korean government, Ministry of Science and ICT (MSIP) [2018R1A2B6009076]
- National Research Foundation of Korea [2018R1A2B6009076] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This article introduces the application of deep learning techniques in musculoskeletal radiology, with a focus on the key architectures, technical background, and challenges of generative adversarial networks (GANs). By generating images with high realism, GANs have the potential to aid in faster imaging across different contrasts and modalities. In addition, key research trends and the challenges of clinical applicability are discussed.
In recent years, deep learning techniques have been applied in musculoskeletal radiology to increase the diagnostic potential of acquired images. Generative adversarial networks (GANs), which are deep neural networks that can generate or transform images, have the potential to aid in faster imaging by generating images with a high level of realism across multiple contrast and modalities from existing imaging protocols. This review introduces the key architectures of GANs as well as their technical background and challenges. Key research trends are highlighted, including: (a) reconstruction of high-resolution MRI; (b) image synthesis with different modalities and contrasts; (c) image enhancement that efficiently preserves high-frequency information suitable for human interpretation; (d) pixel-level segmentation with annotation sharing between domains; and (e) applications to different musculoskeletal anatomies. In addition, an overview is provided of the key issues wherein clinical applicability is challenging to capture with conventional performance metrics and expert evaluation. When clinically validated, GANs have the potential to improve musculoskeletal imaging. (C) RSNA, 2021
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