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Image-domain multi-material decomposition for dual-energy CT with non-convex sparsity regularization
Author(s): Qihui Lyu; Daniel O'Connor; Tianye Niu; Ke Sheng
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Paper Abstract

Dual energy CT (DECT) has the potential to decompose tissues into different materials. However, the classic direct inversion (DI) method for multi-material decomposition (MMD) cannot accurately separate more than two basis materials due to the ill-posed problem and amplified image noise. We proposed a novel integrated MMD method that addresses the piecewise smoothness and intrinsic sparsity property of the decomposition image. The proposed MMD was formulated as an optimization problem including a quadratic data fidelity term, an isotropic total variation term that encourages image smoothness, and a non-convex penalty function that promotes decomposition image sparseness. The mass and volume conservation rule were formulated as the probability simplex constraint. An accelerated primal-dual splitting approach with line search was applied to solve the optimization problem. The proposed method with different penalty functions was compared against DI on a digital phantom, a Catphan○c600 phantom, a Quantitative Imaging phantom, and a pelvis patient. The proposed framework distinctly separated the CT image into up to 12 basis materials plus air with high decomposition accuracy. The cross-talks between two different materials are substantially reduced as shown by the decreased non-diagonal elements of the Normalized Cross Correlation (NCC) matrix. The mean square error of the measured electron densities was reduced by 72.6%. Across all datasets, the proposed method improved the average Volume Fraction (VF) accuracy from 63.9% to 99.8% and increased the diagonality of the NCC matrix from 0.73 to 0.96. Compared with DI, the proposed MMD framework improved decomposition accuracy and material separation.

Paper Details

Date Published: 15 March 2019
PDF: 14 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094903 (15 March 2019); doi: 10.1117/12.2508037
Show Author Affiliations
Qihui Lyu, Univ. of California, Los Angeles (United States)
Daniel O'Connor, Univ. of California, Los Angeles (United States)
Tianye Niu, Zhejiang Univ. School of Medicine (China)
Ke Sheng, Univ. of California, Los Angeles (United States)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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