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Material decomposition using dual-energy CT with unsupervised learning

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Abstract

Material decomposition (MD) is an application of dual-energy computed tomography (DECT) that decomposes DECT images into specific material images. However, the direct inversion method used in MD often amplifies noise in the decomposed material images, resulting in lower image quality. To address this issue, we propose an image-domain MD method based on the concept of deep image prior (DIP). DIP is an unsupervised learning method that can perform different tasks without using a large training dataset with known targets (i.e., basis material images). We retrospectively recruited patients who underwent non-contrast brain DECT scans and investigated the feasibility of using the proposed DIP-based method to decompose DECT images into two (i.e., bone and soft tissue) and three (i.e., bone, soft tissue, and fat) basis materials. We evaluated the decomposed material images in terms of signal-to-noise ratio (SNR) and modulation transfer function (MTF). The proposed DIP-based method showed greater improvement in SNR in the decomposed soft-tissue images compared to the direct inversion method and the iterative method. Moreover, the proposed method produced similar MTF curves in both two- and three-material decompositions. Additionally, the proposed DIP-based method demonstrated better separation ability than the other two studied methods in the case of three-material decomposition. Our results suggest that the proposed DIP-based method is capable of unsupervisedly generating high-quality basis material images from DECT images.

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Funding

This work was supported by MOST 111-2221-E-002-069 from Ministry of Science Technology, Taiwan.

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Authors and Affiliations

  1. Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, No.1, Sec. 1, Jen Ai Rd., Zhongzheng Dist., Taipei City, 100, Taiwan

    Hui-Yu Chang & Hsuan-Ming Huang

  2. Department of Medical Imaging, Changhua Christian Hospital, 135 Nanxiao St., Changhua City, 500, Taiwan

    Chi-Kuang Liu

Authors
  1. Hui-Yu Chang
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  2. Chi-Kuang Liu
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Correspondence to Hsuan-Ming Huang.

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Ethics (IRB No. 230213) approval was approved by Institutional Review Board Committee B Changhua Christian Hospital.

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Chang, HY., Liu, CK. & Huang, HM. Material decomposition using dual-energy CT with unsupervised learning. Phys Eng Sci Med 46, 1607–1617 (2023). https://doi.org/10.1007/s13246-023-01323-7

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