Centre for Modeling and Simulation, Campus Alfred Nobel, Örebro University, 69142 Karlskoga, Sweden.
IEEE Trans Med Imaging. 2012 Jun;31(6):1165-72. doi: 10.1109/TMI.2011.2179308. Epub 2011 Dec 9.
Time resolved three-dimensional (3D) echocardiography generates four-dimensional (3D+time) data sets that bring new possibilities in clinical practice. Image quality of four-dimensional (4D) echocardiography is however regarded as poorer compared to conventional echocardiography where time-resolved 2D imaging is used. Advanced image processing filtering methods can be used to achieve image improvements but to the cost of heavy data processing. The recent development of graphics processing unit (GPUs) enables highly parallel general purpose computations, that considerably reduces the computational time of advanced image filtering methods. In this study multidimensional adaptive filtering of 4D echocardiography was performed using GPUs. Filtering was done using multiple kernels implemented in OpenCL (open computing language) working on multiple subsets of the data. Our results show a substantial speed increase of up to 74 times, resulting in a total filtering time less than 30 s on a common desktop. This implies that advanced adaptive image processing can be accomplished in conjunction with a clinical examination. Since the presented GPU processor method scales linearly with the number of processing elements, we expect it to continue scaling with the expected future increases in number of processing elements. This should be contrasted with the increases in data set sizes in the near future following the further improvements in ultrasound probes and measuring devices. It is concluded that GPUs facilitate the use of demanding adaptive image filtering techniques that in turn enhance 4D echocardiographic data sets. The presented general methodology of implementing parallelism using GPUs is also applicable for other medical modalities that generate multidimensional data.
时分辨三维（3D）超声心动图产生四维（3D+时间）数据集，为临床实践带来新的可能性。然而，与使用时分辨 2D 成像的传统超声心动图相比，四维（4D）超声心动图的图像质量被认为较差。先进的图像处理滤波方法可用于改善图像质量，但这会增加数据处理的负担。最近图形处理单元（GPU）的发展使得通用计算能够高度并行化，这大大缩短了先进图像滤波方法的计算时间。在这项研究中，使用 GPU 对 4D 超声心动图进行了多维自适应滤波。滤波是使用在多个数据子集上工作的 OpenCL（开放计算语言）中的多个内核来完成的。我们的结果表明，速度有了显著提高，最高可达 74 倍，在普通台式计算机上的总滤波时间不到 30 秒。这意味着先进的自适应图像处理可以与临床检查结合使用。由于所提出的 GPU 处理器方法与处理元素的数量呈线性比例，我们预计它将随着处理元素数量的预期未来增加而继续扩展。这与未来超声探头和测量设备的进一步改进后，数据集大小的增加形成对比。结论是，GPU 促进了对多维数据进行滤波处理的需求，从而增强了 4D 超声心动图数据集。使用 GPU 实现并行性的这种通用方法也适用于生成多维数据的其他医学模式。
