A cone-beam reconstruction algorithm for circle-plus-arc data-acquisition geometry

Due to the penetration ability and absorption contrast mechanism, cone-beam X-ray microtomography is a powerful tool in studying 3D microstructures in opaque specimens. In contrast to the conventional parallel and fan-beam geometry, the cone-beam tomography set up is highly desirable for faster data acquisition, build-in magnification, better radiation utilization and easier hardware implementation. However, the major draw back of the cone-beam reconstruction is its computational complexity. In an effort to maximize the reconstruction speed, we have developed a generalized Feldkamp cone-beam reconstruction algorithm to optimize the reconstruction process. We report here the use of curved voxels in a cylindrical coordinate system and mapping tables to further improve the reconstruction efficiency.The generalized Feldkamp cone-beam image reconstruction algorithm is reformulated utilizing mapping table in the discrete domain as: , where .

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A general cone-beam reconstruction algorithm

IEEE Transactions on Medical Imaging ◽

10.1109/42.241876 ◽

1993 ◽

Vol 12 (3) ◽

pp. 486-496 ◽

Cited By ~ 228

Author(s):

G. Wang ◽

T.-H. Lin ◽

P. Cheng ◽

D.M. Shinozaki

Keyword(s):

Reconstruction Algorithm ◽

Cone Beam ◽

Cone Beam Reconstruction

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3D Analytic Cone-Beam Reconstruction for Multiaxial CT Acquisitions

International Journal of Biomedical Imaging ◽

10.1155/2009/538389 ◽

2009 ◽

Vol 2009 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Zhye Yin ◽

Bruno De Man ◽

Jed Pack

Keyword(s):

Cone Angle ◽

Reconstruction Algorithm ◽

Cone Beam ◽

Multiple Sources ◽

Relative Contribution ◽

Cone Beam Reconstruction ◽

Full Scan ◽

Axial Ct ◽

Ct System ◽

Scan Data

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.

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