Unsupervised-learning material decomposition for dual-Energy CT with image-domain data fidelity

Junbo Peng; Huiqiao Xie; Chih-Wei Chang; Justin Roper; Richard L. J. Qiu; Tonghe Wang; Xiangyang Tang; Xiaofeng Yang

doi:10.1117/12.3006936

3 April 2024 Unsupervised-learning material decomposition for dual-Energy CT with image-domain data fidelity

Junbo Peng, Huiqiao Xie, Chih-Wei Chang, Justin Roper, Richard L. J. Qiu, Tonghe Wang, Xiangyang Tang, Xiaofeng Yang

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 1292513 (2024) https://doi.org/10.1117/12.3006936
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Dual-energy CT (DECT) plays a vital role in quantitative imaging applications for its capability of material decomposition. Matrix inversion-based material decomposition suffers from severe degradation of signal-to-noise ratio (SNR) on the resultant images. Iterative decomposition methods perform noise suppression through regularization but the artificial image prior cannot characterize all inherent features of noiseless material-specific images. Supervised-learning material decomposition methods train a neural network to fully depict the mapping relationship between DECT images and decomposed images. However, large-size paired DECT and noiseless material-specific images are not always available in clinical practice, hindering the practice of supervised-learning methods. In this work, we propose a practical unsupervised-learning framework for DECT material decomposition with sinogram fidelity and the preliminary results show the feasibility and potential of the proposed method in quantitative applications.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junbo Peng, Huiqiao Xie, Chih-Wei Chang, Justin Roper, Richard L. J. Qiu, Tonghe Wang, Xiangyang Tang, and Xiaofeng Yang "Unsupervised-learning material decomposition for dual-Energy CT with image-domain data fidelity", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 1292513 (3 April 2024); https://doi.org/10.1117/12.3006936

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KEYWORDS

Signal to noise ratio

Artificial neural networks

Biomedical applications

Clinical practice

Computed tomography

Education and training

Matrices

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