TY - JOUR
T1 - Self-supervised learning for CT image denoising and reconstruction
T2 - a review
AU - Choi, Kihwan
N1 - Publisher Copyright:
© Korean Society of Medical and Biological Engineering 2024. corrected publication 2024.
PY - 2024/11
Y1 - 2024/11
N2 - 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.
AB - 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.
KW - Computed tomography (CT)
KW - Dose reduction
KW - Image denoising
KW - Image reconstruction
KW - Self-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85203673218&partnerID=8YFLogxK
U2 - 10.1007/s13534-024-00424-w
DO - 10.1007/s13534-024-00424-w
M3 - Review article
AN - SCOPUS:85203673218
SN - 2093-9868
VL - 14
SP - 1207
EP - 1220
JO - Biomedical Engineering Letters
JF - Biomedical Engineering Letters
IS - 6
ER -