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arXiv 2025年

作者： Sun, Jianing Duan, Jigang Li, Guangyin Jiang, Xu Zhao, Xing School of Mathematical Sciences Capital Normal University Beijing100048 China Beijing Higher Institution Engineering Research Center of Testing and Imaging Beijing China

Compared to single-source imaging systems, dual-source imaging systems equipped with two cross-distributed scanning beams significantly enhance temporal resolution and capture more comprehensive object scanning information. Nevertheless, the interaction between the two scanning beams introduces more complex scatter signals into the acquired projection data. Existing methods typically model these scatter signals as the sum of cross-scatter and forward scatter, with cross-scatter estimation limited to single-scatter along primary paths. Through experimental measurements on our self-developed micro-focus dual-source imaging system, we observed that the peak ratio of hardware-induced ambient scatter to single-source projection intensity can even exceed 60%, a factor often overlooked in conventional models. To address this limitation, we propose a more comprehensive model that decomposes the total scatter signals into three distinct components: ambient scatter, cross-scatter, and forward scatter. Furthermore, we introduce a cross-scatter kernel superposition (xSKS) module to enhance the accuracy of cross-scatter estimation by modeling both single and multiple cross-scatter events along non-primary paths. Additionally, we employ a fast object-adaptive scatter kernel superposition (FOSKS) module for efficient forward scatter estimation. In Monte Carlo (MC) simulation experiments performed on a custom-designed water-bone phantom, our model demonstrated remarkable superiority, achieving a scatter-to-primary-weighted mean absolute percentage error (SPMAPE) of 1.32%, significantly lower than the 12.99% attained by the state-of-the-art method. Physical experiments further validate the superior performance of our model in correcting scatter artifacts. Copyright © 2025, The Authors. All rights reserved.

关键词： imaging systems

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DLIMD: Dictionary learning based image-domain material decomposition for spectral CT

arXiv

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arXiv 2019年

作者： Wu, Weiwen Yu, Haijun Chen, Peijun Luo, Fulin Liu, Fenglin Wang, Qian Zhu, Yining Zhang, Yanbo Feng, Jian Yu, Hengyong Key Lab of Optoelectronic Technology and Systems Ministry of Education Chongqing University Chongqing400044 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States School of Mathematical Sciences Capital Normal University Beijing100048 China Beijing Higher Institution Engineering Research Center of Testing and Imaging Beijing100048 China PingAn Technology US Research Lab Palo AltoCA94306 United States

The potential huge advantage of spectral computed tomography (CT) is its capability to provide accuracy material identification and quantitative tissue information. This can benefit clinical applications, such as brain angiography, early tumor recognition, etc. To achieve more accurate material components with higher material image quality, we develop a dictionary learning based image-domain material decomposition (DLIMD) for spectral CT in this paper. First, we reconstruct spectral CT image from projections and calculate material coefficients matrix by selecting uniform regions of basis materials from image reconstruction results. Second, we employ the direct inversion (DI) method to obtain initial material decomposition results, and a set of image patches are extracted from the mode-1 unfolding of normalized material image tensor to train a united dictionary by the K-SVD technique. Third, the trained dictionary is employed to explore the similarities from decomposed material images by constructing the DLIMD model. Fourth, more constraints (i.e., volume conservation and the bounds of each pixel within material maps) are further integrated into the model to improve the accuracy of material decomposition. Finally, both physical phantom and preclinical experiments are employed to evaluate the performance of the proposed DLIMD in material decomposition accuracy, material image edge preservation and feature recovery. Copyright © 2019, The Authors. All rights reserved.

关键词： Computerized tomography

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Block matching frame based material reconstruction for spectral CT

arXiv

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arXiv 2018年

作者： Wu, Weiwen Wang, Qian Liu, Fenglin Zhu, Yining Yu, Hengyong Key Lab of Optoelectronic Technology and Systems Ministry of Education Chongqing University Chongqing400044 China Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States School of Mathematical Sciences Capital Normal University Beijing100048 China Beijing Higher Institution Engineering Research Center of Testing and Imaging Beijing100048 China

Spectral computed tomography (CT) has great potential in material identification and decomposition. To achieve high-quality material composition images and further suppress the x-ray beam hardening artifacts, we first propose a one-step material reconstruction model based on Taylor's first-order expansion. Then, we develop a basic material reconstruction method named material simultaneous algebraic reconstruction technique (MSART). Considering the local similarity of each material image, we incorporate a powerful block matching frame (BMF) into the material reconstruction (MR) model and generate a BMF based MR (BMFMR) method. Because the BMFMR model contains the L0-norm problem, we adopt a split-Bregman method for optimization. The numerical simulation and physical phantom experiment results validate the correctness of the material reconstruction algorithms and demonstrate that the BMF regularization outperforms the total variation and no-local mean regularizations. Copyright © 2018, The Authors. All rights reserved.

关键词： Algebra

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Iterative beam hardening correction for multi-material objects

Iterative beam hardening correction for multi-material objec...

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第十四届中国体视学与图像分析学术会议

作者： Zhao Yunsong Li Mengfei Zhang Peng School of Mathematical Sciences Capital Normal University Beijing Higher Institution Engineering Research Center of Testing and Imaging Shenzhen Institute of Advanced Technology

Abstract:In this paper,we propose an iterative beam hardening correction method that is applicable for the case with multiple *** introducing some moderate assumptions on the scanned objects,the beam hardening correction problem is converted into a nonlinear system problem,which is then solved *** main feature of the proposed method is that it corrects the beam hardening artifacts for all the materials composing the scanned *** feature of the proposed method is that it has a high degree of parallelism,which means that the method is suitable for acceleration on parallel ***,the proposed method converges fast,2 or 3 round of iterations are enough to obtain a satisfactory *** experiments with both simulated data and real data verifies its validity.

关键词： poly-energetic X-rays beam-hardening correction iterative reconstruction

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