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Fast Algorithms and Efficient GPU Implementations for the Radon Transform and the Back-Projection Operator Represented as Convolution Operators

机译:作为卷积算子的Radon变换和反投影算子的快速算法和高效GpU实现

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摘要

The Radon transform and its adjoint, the back-projection operator, can both be expressed as convolutions in log-polar coordinates. Hence, fast algorithms for the application of these operators can be constructed by using FFT, if data is resampled at log-polar coordinates. Radon data is typically measured on an equally spaced grid in polar coordinates, and reconstructions are represented (as images) in Cartesian coordinates. Therefore, in addition to FFT, several steps of interpolation have to be conducted in order to apply the Radon transform and the back-projection operator by means of convolutions. However, in comparison to the interpolation conducted in Fourier-based gridding methods, the interpolation performed in the Radon and image domains will typically deal with functions that are substantially less oscillatory. Reasonable reconstruction results can thus be expected using interpolation schemes of moderate order. It also provides better control over the artifacts that can appear due to measurement errors.Both the interpolation and the FFT operations can be efficiently implemented on Graphical Processor Units (GPUs). For the interpolation, it is possible to make use of the fact that linear interpolation is hard-wired on GPUs, meaning that it has the same computational cost as direct memory access. Cubic order interpolation schemes can be constructed by combining linear interpolation steps and this provides important computation speedup.We provide details about how the Radon transform and the back-projection can be implemented efficiently as convolution operators on GPUs. For large data sizes, these algorithms are several times faster than those of other software packages based on GPU implementations of the Radon transform and the back-projection operator. Moreover, the gain in computational speed is substantially higher when comparing against other CPU based algorithms.
机译:Radon变换及其伴随元素(反投影算子)都可以表示为对数极坐标中的卷积。因此,如果在对数极坐标处对数据进行重新采样,则可以通过使用FFT构建用于这些运算符的快速算法。 data数据通常是在极坐标上等间距的网格上测量的,而重建则以笛卡尔坐标表示(作为图像)。因此,除了FFT外,还必须执行几个插值步骤,以便通过卷积应用Radon变换和反投影算符。但是,与在基于傅立叶的网格化方法中进行的插值相比,在Radon和图像域中执行的插值通常会处理实质上没有振荡的函数。因此,使用中等阶数的插值方案可以预期到合理的重建结果。它还可以更好地控制由于测量误差而可能出现的伪像。插值和FFT运算均可在图形处理器单元(GPU)上高效实现。对于插值,可以利用线性插值在GPU上进行硬连线的事实,这意味着线性插值的计算成本与直接内存访问相同。立方阶插值方案可以通过组合线性插值步骤来构建,这可以提供重要的计算速度。我们提供了有关Radon变换和反投影如何作为GPU上的卷积算子有效实现的详细信息。对于大数据量,这些算法比基于Radon变换和反投影算符的GPU实现的其他软件包的算法快几倍。而且,与其他基于CPU的算法相比,计算速度的提高要高得多。

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