Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ACCESS.2014.2340372 | IEEE Access |
Keywords | Field | DocType |
parallel processing,gpu-based acceleration,graphics processing unit,computerised tomography,graphics processing unit-based parallel computing technique,region-of-interest total variation minimization,piecewise constant,computed tomography,graphics processing units,cone-beam geometries,computational geometry,cs-based interior tomography,compressive sensing theory,image reconstruction,piecewise polynomial techniques,compressed sensing,piecewise constant techniques,piecewise polynomial,interior tomography,fan-beam geometries,parallel computing,cs-based interior reconstruction,volumetric image reconstruction,iterative methods,medical image processing,compressive sensing,tomography,graphics | Polynomial,Computer science,Computational science,Artificial intelligence,Interior reconstruction,Piecewise,Compressed sensing,Distributed computing,Iterative reconstruction,Computer vision,Tomography,Acceleration,Graphics processing unit | Journal |
Volume | ISSN | Citations |
2 | 2169-3536 | 3 |
PageRank | References | Authors |
0.41 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Liu | 1 | 4 | 1.46 |
| Yan Luo | 2 | 148 | 19.24 |
| Hengyong Yu | 3 | 293 | 35.54 |