Chillarón Mónica, Quintana-Ortí Gregorio, Vidal Vicente, Verdú Gumersindo
Depto. de Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, 46022 Spain.
Depto. de Ingeniería y Ciencia de Computadores, Universitat Jaume I, Castellón, 12071 Spain.
Comput Methods Programs Biomed. 2020 Sep;193:105488. doi: 10.1016/j.cmpb.2020.105488. Epub 2020 Apr 6.
As Computed Tomography scans are an essential medical test, many techniques have been proposed to reconstruct high-quality images using a smaller amount of radiation. One approach is to employ algebraic factorization methods to reconstruct the images, using fewer views than the traditional analytical methods. However, their main drawback is the high computational cost and hence the time needed to obtain the images, which is critical in the daily clinical practice. For this reason, faster methods for solving this problem are required.
In this paper, we propose a new reconstruction method based on the QR factorization that is very efficient on affordable equipment (standard multicore processors and standard Solid-State Drives) by using Out-Of-Core techniques.
Combining both affordable hardware and the new software proposed in our work, the images can be reconstructed very quickly and with high quality. We analyze the reconstructions using real Computed Tomography images selected from a dataset, comparing the QR method to the LSQR and FBP. We measure the quality of the images using the metrics Peak Signal-To-Noise Ratio and Structural Similarity Index, obtaining very high values. We also compare the efficiency of using spinning disks versus Solid-State Drives, showing how the latter performs the Input/Output operations in a significantly lower amount of time.
The results indicate that our proposed me thod and software are valid to efficiently solve large-scale systems and can be applied to the Computed Tomography reconstruction problem to obtain high-quality images.
由于计算机断层扫描（CT）是一项重要的医学检查，人们已经提出了许多技术来使用较少的辐射重建高质量图像。一种方法是采用代数分解方法来重建图像，与传统的解析方法相比使用更少的视图。然而，它们的主要缺点是计算成本高，因此获取图像所需的时间长，这在日常临床实践中至关重要。因此，需要更快的方法来解决这个问题。
在本文中，我们提出了一种基于QR分解的新重建方法，通过使用核外技术，在经济实惠的设备（标准多核处理器和标准固态硬盘）上非常高效。
结合经济实惠的硬件和我们工作中提出的新软件，可以非常快速且高质量地重建图像。我们使用从数据集中选择的真实CT图像分析重建结果，将QR方法与LSQR和FBP进行比较。我们使用峰值信噪比和结构相似性指数等指标来衡量图像质量，得到了非常高的值。我们还比较了使用旋转磁盘和固态硬盘的效率，展示了后者在显著更短的时间内执行输入/输出操作。
结果表明，我们提出的方法和软件对于有效解决大规模系统是有效的，并且可以应用于CT重建问题以获得高质量图像。
