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GPU-Based Acceleration for Interior Tomography

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3 Author(s)
Rui Liu ; Dept. of Biomed. Eng., Wake Forest Univ. Health Sci., Winston-Salem, NC, USA ; Yan Luo ; Hengyong Yu

The compressive sensing (CS) theory shows that real signals can be exactly recovered from very few samplings. Inspired by the CS theory, the interior problem in computed tomography is proved uniquely solvable by minimizing the region-of-interest's total variation if the imaging object is piecewise constant or polynomial. This is called CS-based interior tomography. However, the CS-based algorithms require high computational cost due to their iterative nature. In this paper, a graphics processing unit (GPU)-based parallel computing technique is applied to accelerate the CS-based interior reconstruction for practical application in both fan-beam and cone-beam geometries. Our results show that the CS-based interior tomography is able to reconstruct excellent volumetric images with GPU acceleration in a few minutes.

Note: Special Section on Emerging Computed Tomography Technologies  

Bar charts of the computational cost for different GPU-based reconstruction methods in fan-beam geometry. The left column is for image size 1024x1024 and the right column is for image size 2048x2048 after 50 iterations. For the top row bar charts, the vertical axis represents the reconstruction time and the unit is second. The bottom row is the speedup comparison between CPU and GPU. The abscissa indicates different projection number used in reconstruction. The left vertical axis is the reconstruction time (in seconds, drawn in logarithmic scale), and the right vertical axis is for the speedup factor in the bottom charts. Bar charts of the computational cost for different GPU-based reconstruction methods in fan-beam geometry. The left column is for image size 1024x1024 and the right column is for image size 2048x2048 after 50 iterations. For the top row bar charts, the vertical axis represents the reconstruction time and the unit is second. The bottom row is the speedup comparison between CPU and GPU. The abscissa indicates different projection number used in reconstruction. The left vertical axis is the reconstruction time (in seconds, drawn in logarithmic scale), and the right vertical axis is for the speedup factor in the bottom charts.

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Access, IEEE  (Volume:2 )
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