Literature DB >> 28901608

Optimization of the geometry and speed of a moving blocker system for cone-beam computed tomography scatter correction.

Xi Chen1,2,3, Luo Ouyang1, Hao Yan4, Xun Jia1, Bin Li5, Qingwen Lyu6, You Zhang1, Jing Wang1.   

Abstract

PURPOSE: X-ray scatter is a significant barrier to image quality improvements in cone-beam computed tomography (CBCT). A moving blocker-based strategy was previously proposed to simultaneously estimate scatter and reconstruct the complete volume within the field of view (FOV) from a single CBCT scan. A blocker consisting of lead stripes is inserted between the X-ray source and the imaging object, and moves back and forth along the rotation axis during gantry rotation. While promising results were obtained in our previous studies, the geometric design and moving speed of the blocker were set empirically. The goal of this work is to optimize the geometry and speed of the moving block system.
METHODS: Performance of the blocker was examined through Monte Carlo (MC) simulation and experimental studies with various geometry designs and moving speeds. All hypothetical designs employed an anthropomorphic pelvic phantom. The scatter estimation accuracy was quantified by using lead stripes ranging from 5 to 100 pixels on the detector plane. An iterative reconstruction based on total variation minimization was used to reconstruct CBCT images from unblocked projection data after scatter correction. The reconstructed image was evaluated under various combinations of lead strip width and interspace (ranging from 10 to 60 pixels) and different moving speed (ranging from 1 to 30 pixels per projection).
RESULTS: MC simulation showed that the scatter estimation error varied from 0.8% to 5.8%. Phantom experiment showed that CT number error in the reconstructed CBCT images varied from 13 to 35. Highest reconstruction accuracy was achieved when the strip width was 20 pixels and interspace was 60 pixels and the moving speed was 15 pixels per projection.
CONCLUSIONS: Scatter estimation can be achieved in a large range of lead strip width and interspace combinations. The moving speed does not have a very strong effect on reconstruction result if it is above 5 pixels per projection. Geometry design of the blocker affected image reconstruction accuracy more. The optimal geometry of the blocker has a strip width of 20 pixels and an interspace three times the strip width, which means 25% detector is covered by the blocker, while the optimal moving speed is 15 pixels per projection.
© 2017 American Association of Physicists in Medicine.

Entities:  

Keywords:  cone-beam CT; imaging artifacts; moving blocker; optimization; scatter correction

Mesh:

Year:  2017        PMID: 28901608      PMCID: PMC5619659          DOI: 10.1002/mp.12326

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  52 in total

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Authors:  J H Siewerdsen; D A Jaffray
Journal:  Med Phys       Date:  2001-02       Impact factor: 4.071

2.  Efficient object scatter correction algorithm for third and fourth generation CT scanners.

Authors:  B Ohnesorge; T Flohr; K Klingenbeck-Regn
Journal:  Eur Radiol       Date:  1999       Impact factor: 5.315

3.  Penalized-likelihood sinogram smoothing for low-dose CT.

Authors:  Patrick J La Rivière
Journal:  Med Phys       Date:  2005-06       Impact factor: 4.071

4.  Scatter correction in cone-beam CT via a half beam blocker technique allowing simultaneous acquisition of scatter and image information.

Authors:  Ho Lee; Lei Xing; Rena Lee; Benjamin P Fahimian
Journal:  Med Phys       Date:  2012-05       Impact factor: 4.071

5.  Scatter correction for cone-beam CT in radiation therapy.

Authors:  Lei Zhu; Yaoqin Xie; Jing Wang; Lei Xing
Journal:  Med Phys       Date:  2009-06       Impact factor: 4.071

6.  Scatter correction method for x-ray CT using primary modulation: phantom studies.

Authors:  Hewei Gao; Rebecca Fahrig; N Robert Bennett; Mingshan Sun; Josh Star-Lack; Lei Zhu
Journal:  Med Phys       Date:  2010-02       Impact factor: 4.071

7.  Robust primary modulation-based scatter estimation for cone-beam CT.

Authors:  Ludwig Ritschl; Rebecca Fahrig; Michael Knaup; Joscha Maier; Marc Kachelrieß
Journal:  Med Phys       Date:  2015-01       Impact factor: 4.071

8.  Systematic measurements of whole-body imaging dose distributions in image-guided radiation therapy.

Authors:  Roger A Halg; Jurgen Besserer; Uwe Schneider
Journal:  Med Phys       Date:  2012-12       Impact factor: 4.071

9.  Monte Carlo simulation of the scattered radiation distribution in diagnostic radiology.

Authors:  J M Boone; J A Seibert
Journal:  Med Phys       Date:  1988 Sep-Oct       Impact factor: 4.071

10.  Clinical introduction of image lag correction for a cone beam CT system.

Authors:  Uros Stankovic; Lennert S Ploeger; Jan-Jakob Sonke; Marcel van Herk
Journal:  Med Phys       Date:  2016-03       Impact factor: 4.071

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Authors:  Baiyu Chen; Erich Kobler; Matthew J Muckley; Aaron D Sodickson; Thomas O'Donnell; Thomas Flohr; Bernhard Schmidt; Daniel K Sodickson; Ricardo Otazo
Journal:  Med Phys       Date:  2019-05-06       Impact factor: 4.071

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Journal:  Phys Med Biol       Date:  2022-05-24       Impact factor: 4.174

5.  4D cone-beam computed tomography (CBCT) using a moving blocker for simultaneous radiation dose reduction and scatter correction.

Authors:  Cong Zhao; Yuncheng Zhong; Xinhui Duan; You Zhang; Xiaokun Huang; Jing Wang; Mingwu Jin
Journal:  Phys Med Biol       Date:  2018-05-29       Impact factor: 3.609

6.  An unsupervised 2D-3D deformable registration network (2D3D-RegNet) for cone-beam CT estimation.

Authors:  You Zhang
Journal:  Phys Med Biol       Date:  2021-03-24       Impact factor: 4.174

7.  Robust moving-blocker scatter correction for cone-beam computed tomography using multiple-view information.

Authors:  Cong Zhao; Xi Chen; Luo Ouyang; Jing Wang; Mingwu Jin
Journal:  PLoS One       Date:  2017-12-21       Impact factor: 3.240

8.  Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motion-compensated reconstruction, biomechanical modeling and deep learning.

Authors:  You Zhang; Xiaokun Huang; Jing Wang
Journal:  Vis Comput Ind Biomed Art       Date:  2019-12-12
  8 in total

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