Fast hybrid CPU- and GPU-based CT reconstruction algorithm using air skipping technique
This paper presents a fast hybrid CPU- and GPU-based CT reconstruction algorithm to reduce the amount of back-projection operation using air skipping involving polygon clipping. The algorithm easily and rapidly selects air areas that have significantly higher contrast in each projection image by app...
Saved in:
| Published in | Journal of X-ray science and technology Vol. 18; no. 3; pp. 221 - 234 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
2010
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0895-3996 1095-9114 1095-9114 |
| DOI | 10.3233/XST-2010-0256 |
Cover
| Summary: | This paper presents a fast hybrid CPU- and GPU-based CT reconstruction algorithm to reduce the amount of back-projection operation using air skipping involving polygon clipping. The algorithm easily and rapidly selects air areas that have significantly higher contrast in each projection image by applying K-means clustering method on CPU, and then generates boundary tables for verifying valid region using segmented air areas. Based on these boundary tables of each projection image, clipped polygon that indicates active region when back-projection operation is performed on GPU is determined on each volume slice. This polygon clipping process makes it possible to use smaller number of voxels to be back-projected, which leads to a faster GPU-based reconstruction method. This approach has been applied to a clinical data set and Shepp-Logan phantom data sets having various ratio of air region for quantitative and qualitative comparison and analysis of our and conventional GPU-based reconstruction methods. The algorithm has been proved to reduce computational time to half without losing any diagnostic information, compared to conventional GPU-based approaches. |
|---|---|
| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0895-3996 1095-9114 1095-9114 |
| DOI: | 10.3233/XST-2010-0256 |