TV-regularized iterative image reconstruction on a mobile C-ARM CT
TV-regularized iterative image reconstruction on a mobile C-ARM CT
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DOI：
10.1117/12.844398
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发表时间：
2010-03
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影响因子：
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通讯作者：
Yongsheng Pan;R. Whitaker;A. Cheryauka;D. Ferguson
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文献类型：
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作者：
Yongsheng Pan;R. Whitaker;A. Cheryauka;D. Ferguson
3D计算机断层扫描在现代社会中得到了广泛的研究和广泛的应用。虽然大多数制造商选择滤波反投影算法（FBP）的准确性和效率，迭代重建方法有一个显着的潜力，提供上级性能不完整，噪声投影数据。然而，迭代方法具有较高的计算成本，这阻碍了它们的实际应用。此外，通常需要正则化来减少噪声的影响。在本文中，我们分析了使用的同时代数重建技术（SART）与总变差（TV）正则化。此外，利用图形硬件来提高SART的速度。NVIDIA的GPU和计算统一设备架构（CUDA）构成了我们计算平台的核心。GPU的实施细节，包括基于光线的前向投影和基于体素的反投影示出。高分辨率的合成和真实的数据的实验结果证明了所提出的框架的准确性和效率。
3D computed tomography has been extensively studied and widely used in modern society. Although most manufacturers choose the filtered backprojection algorithm (FBP) for its accuracy and efficiency, iterative reconstruction methods have a significant potential to provide superior performance for incomplete, noisy projection data. However, iterative methods have a high computational cost, which hinders their practical use. Furthermore, regularization is usually required to reduce the effects of noise. In this paper, we analyze the use of the Simultaneous Algebraic Reconstruction Technique (SART) with total variation (TV) regularization. Additionally, graphics hardware is utilized to increase the speed of SART. NVIDIA's GPU and Compute Unified Device Architecture (CUDA) comprise the core of our computational platform. GPU implementation details, including ray-based forward projection and voxel-based backprojection are illustrated. Experimental results for high-resolution synthetic and real data are provided to demonstrate the accuracy and efficiency of the proposed framework.