Mr. Wei Chen Profile

Wei Chen

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Proceedings Article | 23 March 2010 Paper

Accelerating ring artifact correction for flat-detector CT using the CUDA framework

W. Chen, D. Prell, Y, Kyriakou, W. Kalender

Proceedings Volume 7622, 76223A (2010) https://doi.org/10.1117/12.844254

KEYWORDS: Digital filtering, Image filtering, Nonlinear filtering, Computed tomography, Sensors, Chemical elements, Visualization, Image quality, Medical imaging, Image processing

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Ring artifacts often appear in flat-detector CT because of imperfect or defect detector elements or calibration. In high-spatial resolution CT images reducing such artifacts becomes a necessity. In this paper, we used the post-processing ring correction in polar coordinates (RCP)1 to eliminate the ring artifacts. The median filter is applied to the uncorrected images in polar coordinates and ring artifacts are extracted from the original images. The algorithm has a very high computational cost due to the time-expensive median filtering and coordinate transformation on CPUs. Graphics processing units (GPUs)ca n be seen as parallel co-processors with high computational power. All steps of the RCP algorithm were implemented with CUDA2(Compute Unified Device Architecture, NVIDIA). We introduced a new GPU-based branchless vectorized median (BVM)filter. 3, 4 This algorithm is based on minmax sorting and keeps track of a sorted array from which values are deleted and to which new values are inserted. For comparison purpose a modified pivot median filter5 on GPUs was presented, which compares a pivot element to all other values and recursively finds the median element. We evaluated the performance of the RCP method using 512 slices, each slice consisted of 512×512 pixels. This post-processing method efficiently reduces ring artifacts in the reconstructed images and improves image quality. Our CUDAbased RCP is up to 13.6 times faster than the optimized CPU-based (single core)r outine. Comparing our two GPU-based median filters showed a performance benefit by roughly 60% when switching from Pivot to BVM code. The main reason is that the BVM algorithm is branchless and makes use of data-level parallelism. The BVM method is better suited to the model of modern graphics processing. A multi-GPU solution showed that the performance scaled nearly linearly.

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