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28 May 2019 Image-domain multi-material decomposition using a union of cross-material models
Zhipeng Li, Saiprasad Ravishankar, Yong Long
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Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 1107210 (2019) https://doi.org/10.1117/12.2533622
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States
Abstract
Penalized weighted-least squares (PWLS) with learned material priors is a promising way to achieve high quality basis material images using dual energy CT (DECT). We propose a new image-domain multi-material decomposition (MMD) method that combines PWLS estimation with regularization based on a union of learned crossmaterial transforms (CULTRA) model. Numerical experiments with the XCAT phantom show that the proposed method significantly improves the basis materials’ image quality over direct matrix inversion and PWLS decomposition with regularization involving a total nuclear norm (TNV) term and a ℓ0 norm term (PWLS-TNV-ℓ0).
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Zhipeng Li, Saiprasad Ravishankar, and Yong Long "Image-domain multi-material decomposition using a union of cross-material models", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107210 (28 May 2019); https://doi.org/10.1117/12.2533622
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KEYWORDS
Image quality
Computed tomography
Dual energy imaging
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