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Implementation of the computer tomography parallel algorithms with the incomplete set of data
PeerJ Computer Science ( IF 3.8 ) Pub Date : 2021-02-24 , DOI: 10.7717/peerj-cs.339
Mariusz Pleszczyński

Computer tomography has a wide field of applicability; however, most of its applications assume that the data, obtained from the scans of the examined object, satisfy the expectations regarding their amount and quality. Unfortunately, sometimes such expected data cannot be achieved. Then we deal with the incomplete set of data. In the paper we consider an unusual case of such situation, which may occur when the access to the examined object is difficult. The previous research, conducted by the author, showed that the CT algorithms can be used successfully in this case as well, but the time of reconstruction is problematic. One of possibilities to reduce the time of reconstruction consists in executing the parallel calculations. In the analyzed approach the system of linear equations is divided into blocks, such that each block is operated by a different thread. Such investigations were performed only theoretically till now. In the current paper the usefulness of the parallel-block approach, proposed by the author, is examined. The conducted research has shown that also for an incomplete data set in the analyzed algorithm it is possible to select optimal values of the reconstruction parameters. We can also obtain (for a given number of pixels) a reconstruction with a given maximum error. The paper indicates the differences between the classical and the examined problem of CT. The obtained results confirm that the real implementation of the parallel algorithm is also convergent, which means it is useful.

中文翻译:

具有不完整数据集的计算机层析成像并行算法的实现

计算机断层扫描具有广泛的适用性。但是,它的大多数应用程序都假定从检查对象的扫描获得的数据满足有关其数量和质量的期望。不幸的是,有时无法获得这样的预期数据。然后,我们处理不完整的数据集。在本文中,我们考虑了这种情况的异常情况,这种情况可能在难以访问被检查对象时发生。作者进行的先前研究表明,在这种情况下,CT算法也可以成功使用,但是重建时间存在问题。减少重建时间的一种可能性在于执行并行计算。在分析的方法中,线性方程组分为多个块,这样每个块都由不同的线程操作。到目前为止,这种研究仅在理论上进行。在本文中,作者提出了并行块方法的有用性。进行的研究表明,对于分析算法中不完整的数据集,也有可能选择重建参数的最佳值。我们还可以获得(对于给定数量的像素)具有给定最大误差的重构。本文指出了经典的和已检查的CT问题之间的差异。获得的结果证实了并行算法的实际实现也是收敛的,这意味着它是有用的。由作者提出,进行了审查。进行的研究表明,对于分析算法中不完整的数据集,也有可能选择重建参数的最佳值。我们还可以获得(对于给定数量的像素)具有给定最大误差的重构。本文指出了经典的和已检查的CT问题之间的差异。获得的结果证实了并行算法的实际实现也是收敛的,这意味着它是有用的。由作者提出,进行了审查。进行的研究表明,对于分析算法中不完整的数据集,也有可能选择重建参数的最佳值。我们还可以获得(对于给定数量的像素)具有给定最大误差的重构。本文指出了经典的和已检查的CT问题之间的差异。获得的结果证实了并行算法的实际实现也是收敛的,这意味着它是有用的。本文指出了经典的和已检查的CT问题之间的差异。获得的结果证实了并行算法的实际实现也是收敛的,这意味着它是有用的。本文指出了经典的和已检查的CT问题之间的差异。获得的结果证实了并行算法的实际实现也是收敛的,这意味着它是有用的。
更新日期:2021-02-24
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