Artifact reduction using segmentation constrained RPCA for CT

Y. Kim; D. Choi; S. Lim; S. Cho

doi:10.1117/12.2523642

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27 March 2019 Artifact reduction using segmentation constrained RPCA for CT

Y. Kim, D. Choi, S. Lim, S. Cho

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Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110500H (2019) https://doi.org/10.1117/12.2523642
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

In this study, we aim to separate the ghost artifacts from the limited angle CT image by using Robust Principle Component Analysis (RPCA) and thus improve the reconstructed CT images. Conventionally, RPCA method separates the foreground and the background. Often, the background is assumed as static or quasi-static. When applied to limited angle CT images, the artifacts are considered as quasi-static background whereas the anatomical structures are considered foreground. Thus, RPCA is performed to segment the foreground from the background. Finally, different post-reconstruction de-noising parameters are applied to each foreground and background to remove the artifact effectively.

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Y. Kim, D. Choi, S. Lim, and S. Cho "Artifact reduction using segmentation constrained RPCA for CT", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500H (27 March 2019); https://doi.org/10.1117/12.2523642

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