Unsupervised-learning material decomposition for dual-energy CT with image-domain data fidelity Conference Paper
| Authors: | Peng, J.; Xie, H.; Chang, C. W.; Roper, J.; Qiu, R. L. J.; Wang, T.; Tang, X.; Yang, X. |
| Title: | Unsupervised-learning material decomposition for dual-energy CT with image-domain data fidelity |
| Conference Title: | SPIE Medical Imaging 2024: Physics of Medical Imaging |
| 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. © 2024 SPIE. |
| Keywords: | unsupervised learning; computerized tomography; medical imaging; quantitative imaging; signal to noise ratio; learning systems; iterative methods; noise suppression; dual-energy ct; imaging applications; data fidelity; decomposition methods; image domain; learning materials; material decomposition; matrix inversions |
| Journal Title | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
| Volume: | 12925 |
| Conference Dates: | 2024 Feb 19-22 |
| Conference Location: | San Deigo, CA |
| ISBN: | 1605-7422 |
| Publisher: | SPIE |
| Date Published: | 2024-01-01 |
| Start Page: | 1292513 |
| Language: | English |
| DOI: | 10.1117/12.3006936 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Conference paper -- Source: Scopus |
