3D Analytic Cone-Beam Reconstruction for Multiaxial CT Acquisitions
A conventional 3rd generation Computed Tomography (CT) system with a single circular source trajectory is limited in terms of longitudinal scan coverage since extending the scan coverage beyond 40 mm results in significant cone-beam artifacts. A multiaxial CT acquisition is achieved by combining mul...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2009-01-01
|
| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2009/538389 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832558474383327232 |
|---|---|
| author | Zhye Yin Bruno De Man Jed Pack |
| author_facet | Zhye Yin Bruno De Man Jed Pack |
| author_sort | Zhye Yin |
| collection | DOAJ |
| description | A conventional 3rd generation Computed Tomography (CT) system with a single circular source trajectory is limited in terms of longitudinal scan coverage since extending the scan coverage beyond 40 mm results in significant cone-beam artifacts. A multiaxial CT acquisition is achieved by combining multiple sequential 3rd generation axial scans or by performing a single axial multisource CT scan with multiple longitudinally offset sources. Data from multiple axial scans or multiple sources provide complementary information. For full-scan acquisitions, we present a window-based 3D analytic cone-beam reconstruction algorithm by tessellating data from neighboring axial datasets. We also show that multi-axial CT acquisition can extend the axial scan coverage while minimizing cone-beam artifacts. For half-scan acquisitions, one cannot take advantage of conjugate rays. We propose a cone-angle dependent weighting approach to combine multi-axial half-scan data. We compute the relative contribution from each axial dataset to each voxel based on the X-ray beam collimation, the respective cone-angles, and the spacing between the axial scans. We present numerical experiments to demonstrate that the proposed techniques successfully reduce cone-beam artifacts at very large volumetric coverage. |
| format | Article |
| id | doaj-art-aec8020859644b90b6496feaa5592e3e |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2009-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-aec8020859644b90b6496feaa5592e3e2025-02-03T01:32:18ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962009-01-01200910.1155/2009/5383895383893D Analytic Cone-Beam Reconstruction for Multiaxial CT AcquisitionsZhye Yin0Bruno De Man1Jed Pack2CT and Xray systems and applications laboratory, GE Global Research, Niskayuna, NY 12309, USACT and Xray systems and applications laboratory, GE Global Research, Niskayuna, NY 12309, USACT and Xray systems and applications laboratory, GE Global Research, Niskayuna, NY 12309, USAA conventional 3rd generation Computed Tomography (CT) system with a single circular source trajectory is limited in terms of longitudinal scan coverage since extending the scan coverage beyond 40 mm results in significant cone-beam artifacts. A multiaxial CT acquisition is achieved by combining multiple sequential 3rd generation axial scans or by performing a single axial multisource CT scan with multiple longitudinally offset sources. Data from multiple axial scans or multiple sources provide complementary information. For full-scan acquisitions, we present a window-based 3D analytic cone-beam reconstruction algorithm by tessellating data from neighboring axial datasets. We also show that multi-axial CT acquisition can extend the axial scan coverage while minimizing cone-beam artifacts. For half-scan acquisitions, one cannot take advantage of conjugate rays. We propose a cone-angle dependent weighting approach to combine multi-axial half-scan data. We compute the relative contribution from each axial dataset to each voxel based on the X-ray beam collimation, the respective cone-angles, and the spacing between the axial scans. We present numerical experiments to demonstrate that the proposed techniques successfully reduce cone-beam artifacts at very large volumetric coverage.http://dx.doi.org/10.1155/2009/538389 |
| spellingShingle | Zhye Yin Bruno De Man Jed Pack 3D Analytic Cone-Beam Reconstruction for Multiaxial CT Acquisitions International Journal of Biomedical Imaging |
| title | 3D Analytic Cone-Beam Reconstruction for Multiaxial CT Acquisitions |
| title_full | 3D Analytic Cone-Beam Reconstruction for Multiaxial CT Acquisitions |
| title_fullStr | 3D Analytic Cone-Beam Reconstruction for Multiaxial CT Acquisitions |
| title_full_unstemmed | 3D Analytic Cone-Beam Reconstruction for Multiaxial CT Acquisitions |
| title_short | 3D Analytic Cone-Beam Reconstruction for Multiaxial CT Acquisitions |
| title_sort | 3d analytic cone beam reconstruction for multiaxial ct acquisitions |
| url | http://dx.doi.org/10.1155/2009/538389 |
| work_keys_str_mv | AT zhyeyin 3danalyticconebeamreconstructionformultiaxialctacquisitions AT brunodeman 3danalyticconebeamreconstructionformultiaxialctacquisitions AT jedpack 3danalyticconebeamreconstructionformultiaxialctacquisitions |