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| A Study of Double TV4 Regularization Based Spectral CT Projection
Domain Material Decomposition Method |
| YU Xin-li1, 2, KONG Hui-hua1, 2*, ZHANG Ran1, 2 |
1. School of Mathematics, North University of China, Taiyuan 030051, China
2. National Key Laboratory of Photoelectric Dynamic Testing Technology and Instrument for Extreme Environments, Taiyuan 030051, China
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Abstract Spectral computed tomography (CT) can distinguish different material compositions by utilizing the differences in material attenuation characteristics under various X-ray energies. Projection-based material decomposition is a commonly used method, which consists of two steps: projection-domain decomposition and basis-material image reconstruction. To address the susceptibility of this method to noise contamination during decomposition, this study proposes a double-regularized two-step decomposition framework that simultaneously incorporates four-directional total variation (TV4) regularization priors into both material decomposition and basis image reconstruction. Extending conventional total variation (TV) to four-directional gradients, TV4 demonstrates enhanced capability in comprehensively capturing multi-directional edges within material images while achieving joint optimization for noise suppression, thereby exhibiting superior robustness in low-dose or high-noise scenarios. Experimental validation was conducted using multi-energy channel projection data from both simulated phantoms and preclinical in vivo mice. In the projection decomposition phase, the proposed TV4 algorithm was compared with conventional LS and SR-TF algorithms in terms of denoising performance. To further evaluate the material decomposition accuracy, basis material images obtained through different regularization strategies were quantitatively compared using root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) metrics. Results demonstrate that the proposed algorithm achieves a clear separation of basis material images, attaining the highest PSNR values and the lowest RMSE values among all compared methods. These findings confirm the method's effectiveness in suppressing noise and artifact interference during decomposition while significantly enhancing basis material image quality.
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Received: 2025-04-03
Accepted: 2025-07-16
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Corresponding Authors:
KONG Hui-hua
E-mail: huihuak@163.com
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