An Sequence Subsets Simultaneous Algebraic Reconstruction Techniques
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摘要: 图像重建迭代算法的主要缺点是计算量大,重建速度慢。为减少计算时间,Hudson等提出了有序子集算法,由于该算法在每次迭代时使用固定的子集个数,重建图像的质量主要依赖于迭代步中的子集数。本文提出序列子集联合代数重建技术,在每次迭代后减少使用的子集个数,这样在加速图像收敛的同时恢复重建图像的各种频率元素。实验结果表明序列子集联合代数重建技术可在少数次迭代后提供较高质量的重建图像,且对噪声数据不敏感。Abstract: A major drawback of the iterative image reconstruction algorithms is their high computational cost. To decrease computation time, an Ordered Subsets (OS) method was proposed by Hudson et al. Because the OS algorithm uses a fixed number of subsets for each iteration, the quality of images depends upon the number of subsets. In this paper, we develop a new algorithm called the sequence subsets SART, which decrease the number of subsets after each iteration. Thus we not only accelerate convergence of the reconstructed image but also recover various frequency components of the reconstructed image in early iterative steps. Experimental results show that the proposed sequence subsets SART can provide higher quality reconstructed images that are very insensitive to noise at a small number of iterations.
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Keywords:
- iterative algorithm /
- image reconstruction /
- ordered subsets /
- sequence subsets
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