GPU based parallel acceleration for fast C-arm cone-beam CT reconstruction
Abstract Background With the introduction of Flat Panel Detector technology, cone-beam CT (CBCT) has become a novel image modality, and widely applied in clinical practices. C-arm mounted CBCT has shown extra suitability in image guided interventional surgeries. During practice, how to acquire high...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-06-01
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| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12938-018-0506-4 |
| _version_ | 1818450723488137216 |
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| author | Ken Chen Cheng Wang Jing Xiong Yaoqin Xie |
| author_facet | Ken Chen Cheng Wang Jing Xiong Yaoqin Xie |
| author_sort | Ken Chen |
| collection | DOAJ |
| description | Abstract Background With the introduction of Flat Panel Detector technology, cone-beam CT (CBCT) has become a novel image modality, and widely applied in clinical practices. C-arm mounted CBCT has shown extra suitability in image guided interventional surgeries. During practice, how to acquire high resolution and high quality 3D images with the real time requirement of clinical applications remain challenging. Methods In this paper, we propose a GPU based accelerated method for fast C-arm CBCT 3D image reconstructions. A filtered back projection method is optimized and implemented with GPU parallel acceleration technique. A distributed system is designed to make full use of the image acquisition consumption to hide the reconstruction delay to further improve system performance. Results With the acceleration both in algorithm and system design, we show that our method significantly increases system efficiency. The optimized GPU accelerated FDK algorithm improves the reconstruction efficiency. The system performance is further enhanced with the proposed system design by 26% and reconstruction delay is accelerated by 2.1 times when 90 frames of projections are used. When the number of frames used increases to 120, the numbers are 39% and 3.3 times. We also show that when the projection acquisition consumption increases, the reconstruction acceleration rate increases significantly. |
| first_indexed | 2024-12-14T20:55:50Z |
| format | Article |
| id | doaj.art-f76d0eead3394db49e75c5fc65a5feb3 |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-12-14T20:55:50Z |
| publishDate | 2018-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-f76d0eead3394db49e75c5fc65a5feb32022-12-21T22:47:41ZengBMCBioMedical Engineering OnLine1475-925X2018-06-0117111410.1186/s12938-018-0506-4GPU based parallel acceleration for fast C-arm cone-beam CT reconstructionKen Chen0Cheng Wang1Jing Xiong2Yaoqin Xie3Shenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhen Institute of Advanced Technology, Chinese Academy of SciencesAbstract Background With the introduction of Flat Panel Detector technology, cone-beam CT (CBCT) has become a novel image modality, and widely applied in clinical practices. C-arm mounted CBCT has shown extra suitability in image guided interventional surgeries. During practice, how to acquire high resolution and high quality 3D images with the real time requirement of clinical applications remain challenging. Methods In this paper, we propose a GPU based accelerated method for fast C-arm CBCT 3D image reconstructions. A filtered back projection method is optimized and implemented with GPU parallel acceleration technique. A distributed system is designed to make full use of the image acquisition consumption to hide the reconstruction delay to further improve system performance. Results With the acceleration both in algorithm and system design, we show that our method significantly increases system efficiency. The optimized GPU accelerated FDK algorithm improves the reconstruction efficiency. The system performance is further enhanced with the proposed system design by 26% and reconstruction delay is accelerated by 2.1 times when 90 frames of projections are used. When the number of frames used increases to 120, the numbers are 39% and 3.3 times. We also show that when the projection acquisition consumption increases, the reconstruction acceleration rate increases significantly.http://link.springer.com/article/10.1186/s12938-018-0506-4Image guided therapyFast reconstructionCBCTGPU |
| spellingShingle | Ken Chen Cheng Wang Jing Xiong Yaoqin Xie GPU based parallel acceleration for fast C-arm cone-beam CT reconstruction BioMedical Engineering OnLine Image guided therapy Fast reconstruction CBCT GPU |
| title | GPU based parallel acceleration for fast C-arm cone-beam CT reconstruction |
| title_full | GPU based parallel acceleration for fast C-arm cone-beam CT reconstruction |
| title_fullStr | GPU based parallel acceleration for fast C-arm cone-beam CT reconstruction |
| title_full_unstemmed | GPU based parallel acceleration for fast C-arm cone-beam CT reconstruction |
| title_short | GPU based parallel acceleration for fast C-arm cone-beam CT reconstruction |
| title_sort | gpu based parallel acceleration for fast c arm cone beam ct reconstruction |
| topic | Image guided therapy Fast reconstruction CBCT GPU |
| url | http://link.springer.com/article/10.1186/s12938-018-0506-4 |
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