Literature DB >> 9688156

An efficient Fourier method for 3-D radon inversion in exact cone-beam CT reconstruction.

S Schaller1, T Flohr, P Steffen.   

Abstract

The radial derivative of the three-dimensional (3-D) radon transform of an object is an important intermediate result in many analytically exact cone-beam reconstruction algorithms. We briefly review Grangeat's approach for calculating radon derivative data from cone-beam projections and then present a new, efficient method for 3-D radon inversion, i.e., reconstruction of the image from the radial derivative of the 3-D radon transform, called direct Fourier inversion (DFI). The method is based directly on the 3-D Fourier slice theorem. From the 3-D radon derivative data, which is assumed to be sampled on a spherical grid, the 3-D Fourier transform of the object is calculated by performing fast Fourier transforms (FFT's) along radial lines in the radon space. Then, an interpolation is performed from the spherical to a Cartesian grid using a 3-D gridding step in the frequency domain. Finally, this 3-D Fourier transform is transformed back to the spatial domain via 3-D inverse FFT. The algorithm is computationally efficient with complexity in the order of N3 logN. We have done reconstructions of simulated 3-D radon derivative data assuming sampling conditions and image quality requirements similar to those in medical computed tomography (CT).

Mesh:

Year:  1998        PMID: 9688156     DOI: 10.1109/42.700736

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  4 in total

Review 1.  High resolution X-ray computed tomography: an emerging tool for small animal cancer research.

Authors:  M J Paulus; S S Gleason; S J Kennel; P R Hunsicker; D K Johnson
Journal:  Neoplasia       Date:  2000 Jan-Apr       Impact factor: 5.715

2.  Compensating the intensity fall-off effect in cone-beam tomography by an empirical weight formula.

Authors:  Zikuan Chen; Vince D Calhoun; Shengjiang Chang
Journal:  Appl Opt       Date:  2008-11-10       Impact factor: 1.980

3.  Deep Encoder-Decoder Adversarial Reconstruction(DEAR) Network for 3D CT from Few-View Data.

Authors:  Huidong Xie; Hongming Shan; Ge Wang
Journal:  Bioengineering (Basel)       Date:  2019-12-09

4.  Low Dose CT Perfusion With K-Space Weighted Image Average (KWIA).

Authors:  Chenyang Zhao; Thomas Martin; Xingfeng Shao; Jeffry R Alger; Vinay Duddalwar; Danny J J Wang
Journal:  IEEE Trans Med Imaging       Date:  2020-11-30       Impact factor: 10.048

  4 in total

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