Artifacts in computerized tomography (CT), such as metal streak effects, can also be reduced using the iterative
reconstruction approach. The main issue in iterative method is the computation cost by efficient implementation of the
forward and back-projection operations, which are the dominant cost in all iterative reconstruction algorithms. We
designed a field programmable gate array (FPGA)-based operators for iterative forward and back-projection method to
solve the artifact model. The projection method in CT reconstruction is to retrieve the volumetric image based on
observed projection image and makes reconstruction errors minimize. The FPGA-based operators contain only the
iterative reconstruction operations from tomographic projections, and the filtering of detector data and the geometry
correction between detector and object are done by host CPU processor. For FPGA design, we used Impulse C package,
C-to-FPGA tool including the use of streaming and pipelining for high performance. We evaluated the FPGA-based
projection on Shepp-Logan phantom data with metal streak artifact. Simulation results show that the FPGA-based
operators can reduce the computation time of iterative reconstruction, while still providing accuracy comparable to CPU
or GPU-based reconstruction.
The scattering noise artifact resulted in low-dose projection in repetitive cone-beam CT (CBCT) scans decreases the
image quality and lessens the accuracy of the diagnosis. To improve the image quality of low-dose CT imaging, the
statistical filtering is more effective in noise reduction. However, image filtering and enhancement during the entire
reconstruction process exactly may be challenging due to high performance computing. The general reconstruction
algorithm for CBCT data is the filtered back-projection, which for a volume of 512×512×512 takes up to a few minutes
on a standard system. To speed up reconstruction, massively parallel architecture of current graphical processing unit
(GPU) is a platform suitable for acceleration of mathematical calculation. In this paper, we focus on accelerating wavelet
denoising and Feldkamp-Davis-Kress (FDK) back-projection using parallel processing on GPU, utilize compute unified
device architecture (CUDA) platform and implement CBCT reconstruction based on CUDA technique. Finally, we
evaluate our implementation on clinical tooth data sets. Resulting implementation of wavelet denoising is able to process
a 1024×1024 image within 2 ms, except data loading process, and our GPU-based CBCT implementation reconstructs a
512×512×512 volume from 400 projection data in less than 1 minute.
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