Depto. de Ingeniería y Ciencia de Computadores, Universitat Jaume I, Castellón, 12.071, Spain.
Instituto de Seguridad Industrial, Radiofísica y Medioambiental, Universitat Politècnica de València, Valencia, 46.022, Spain.
Comput Methods Programs Biomed. 2022 May;218:106725. doi: 10.1016/j.cmpb.2022.106725. Epub 2022 Mar 2.
Since Computed Tomography (CT) is one of the most widely used medical imaging tests, it is essential to work on methods that reduce the radiation the patient is exposed to. Although there are several possible approaches to achieve this, we focus on reducing the exposure time through sparse sampling. With this approach, efficient algebraic methods are needed to be able to generate the images in real time, and since their computational cost is high, using high-performance computing is essential.
In this paper we present a GPU (Graphics Processing Unit) software for solving the CT image reconstruction problem using the QR factorization performed with out-of-core (OOC) techniques. This implementation is optimized to reduce the data transfer times between disk, CPU, and GPU, as well as to overlap input/output operations and computations.
The experimental study shows that a block cache stored on main page-locked memory is more efficient than using a cache on GPU memory or mirroring it in both GPU and CPU memory. Compared to a CPU version, this implementation is up to 6.5 times faster, providing an improved image quality when compared to other reconstruction methods.
The software developed is an optimized version of the QR factorization for GPU that allows the algebraic reconstruction of CT images with high quality and resolution, with a performance that can be compared with state-of-the-art methods used in clinical practice. This approach allows reducing the exposure time of the patient and thus the radiation dose.
由于计算机断层扫描（CT）是最广泛使用的医学成像测试之一，因此必须致力于研究降低患者所受辐射的方法。虽然有几种可能的方法可以实现这一目标，但我们专注于通过稀疏采样来缩短曝光时间。通过这种方法，需要高效的代数方法来实时生成图像，由于其计算成本很高，因此使用高性能计算是必不可少的。
在本文中，我们提出了一种使用 GPU（图形处理单元）软件，通过使用核外（OOC）技术执行 QR 分解来解决 CT 图像重建问题。该实现经过优化，可以减少磁盘、CPU 和 GPU 之间的数据传输时间，以及重叠输入/输出操作和计算。
实验研究表明，存储在主分页锁定内存中的块缓存比在 GPU 内存中使用缓存或在 GPU 和 CPU 内存中镜像缓存更有效。与 CPU 版本相比，此实现的速度快了 6.5 倍，与其他重建方法相比，提供了更高质量的图像。
开发的软件是 GPU 上的 QR 分解的优化版本，允许以高质量和高分辨率进行 CT 图像的代数重建，其性能可与临床实践中使用的最先进方法相媲美。这种方法可以缩短患者的曝光时间，从而降低辐射剂量。
