Kim, Yongsoo Lee, Jung-woo Lee, Byung Chul Seo, Hyunseok 2025-11-06T05:31:27Z 2025-11-06T05:31:27Z 2025-11-06 2025-11 0094-2405 https://pubs.kist.re.kr/handle/201004/153405 Background It is easy to find computed tomography (CT) images that include metals such as implants, bone plates, and bone shafts. These metals replacing body parts, cause serious artifacts in the CT image originated by x-ray beam-hardening. Traditionally, CT physics-based image processing techniques were empirically applied to reduce metal artifacts (MAs). Recently, with the great success of deep learning, many studies on metal artifact reduction (MAR) using convolutional neural networks (CNNs) have been introduced. Purpose Most of them commonly meet a challenge to obtain ground truth images for MAR in clinical practice. Thus, for effective MAR without ground truth images, we propose a residual MA model for unsupervised deep learning scheme in CT images.Methods In the 1st stage, MAs are extracted by CT physics-inspired residual model, which is enabled by the residual learning scheme. The result of the 1st stage is fed into the input for the 2nd stage, where artifacts that are not reduced enough in the 1st stage are further removed by the contrastive learning scheme. Then, the networks in the 2nd stage can easily recognize the original structures due to primary artifact reduction in the 1st stage and can properly refine the image.Results Our model was validated on three datasets. The results show that the proposed model outperforms other MAR models, preserving both original body and metal structures while reducing MAs effectively.Conclusions We hope that this unsupervised learning model can contribute to good achievements in the MAR field while overcoming the limitations of data construction. English American Association of Physicists in Medicine Residual Metal Artifact Reduction in CT Images: An Unsupervised Residual and Contrastive Learning Approach for Preserving Metal Structures Article 10.1002/mp.70078 1 Medical Physics, v.52, no.11 Medical Physics 52 11 N scie scopus 001602047400001 2-s2.0-105020246117 Radiology, Nuclear Medicine & Medical Imaging Radiology, Nuclear Medicine & Medical Imaging Article RAY COMPUTED-TOMOGRAPHY U-NET NETWORK CT Physics metal artifact reduction unsupervised learning