In magnetic resonance imaging, the fidelity of image reconstruction is an important criterion. It has been suggested that the infinite-extent sinc kernel is the ideal interpolation kernel for ensuring the reconstruction quality of non-Cartesian trajectories. However, the application of the sinc function has been limited owing to its computational overheads. Recently, graphics processing units (GPUs) have been employed as fast computation tools because of their efficient and versatile parallel computation abilities. We implemented an accelerated convolution function with the sinc kernel using GPUs computing and evaluated the reconstruction performance. The computation time was significantly improved: Computation using the proposed method was approximately 270 times faster than that on a central processing unit (CPU) and approximately 4.6 times faster than that on a CPU optimized by level-3 Basic Linear Algebra Subprograms. The images reconstructed using the fast sinc function exhibited no adverse errors at all matrix sizes (resolutions). The total reconstruction time was approximately 0.3–3 s for all matrices, indicating that the sinc function could be a practical option for image reconstruction. Ultimately, its application would present a fundamental improvement to the performance of image reconstruction, and the GPU implementation of the convolution function with the sinc kernel could resolve various challenges in image data processing.

中文翻译：

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使用图形处理单元加速计算以实时应用 sinc 函数

在磁共振成像中，图像重建的保真度是一个重要的标准。有人建议无限范围的正弦核是保证非笛卡尔轨迹重建质量的理想插值核。然而，由于其计算开销，sinc 函数的应用受到限制。最近，图形处理单元 (GPU) 因其高效且通用的并行计算能力而被用作快速计算工具。我们使用 GPU 计算实现了带有 sinc 内核的加速卷积函数，并评估了重建性能。计算时间显着改善：使用所提出方法的计算速度比中央处理器 (CPU) 快约 270 倍，约快 4。比通过 3 级基本线性代数子程序优化的 CPU 快 6 倍。使用快速 sinc 函数重建的图像在所有矩阵大小（分辨率）下都没有出现不利错误。所有矩阵的总重建时间约为 0.3-3 秒，表明 sinc 函数可能是图像重建的实用选择。最终，它的应用将对图像重建的性能产生根本性的改进，并且带有 sinc 内核的卷积函数的 GPU 实现可以解决图像数据处理中的各种挑战。所有矩阵的 3-3 s，表明 sinc 函数可能是图像重建的实用选项。最终，它的应用将对图像重建的性能产生根本性的改进，并且带有 sinc 内核的卷积函数的 GPU 实现可以解决图像数据处理中的各种挑战。所有矩阵的 3-3 s，表明 sinc 函数可能是图像重建的实用选项。最终，它的应用将对图像重建的性能产生根本性的改进，并且带有 sinc 内核的卷积函数的 GPU 实现可以解决图像数据处理中的各种挑战。