Literature DB >> 26009495

Scatter Reduction and Correction for Dual-Source Cone-Beam CT Using Prepatient Grids.

Lei Ren1, Yingxuan Chen2, You Zhang2, William Giles3, Jianyue Jin4, Fang-Fang Yin5.   

Abstract

PURPOSE: Scatter significantly limits the application of the dual-source cone-beam computed tomography by inducing scatter artifacts and degrading contrast-to-noise ratio, Hounsfield-unit accuracy, and image uniformity. Although our previously developed interleaved acquisition mode addressed the cross scatter between the 2 X-ray sources, it doubles the scanning time and doesn't address the forward scatter issue. This study aims to develop a prepatient grid system to address both forward scatter and cross scatter in the dual-source cone-beam computed tomography.
METHODS: Grids attached to both X-ray sources provide physical scatter reduction during the image acquisition. Image data were measured in the unblocked region, while both forward scatter and cross scatter were measured in the blocked region of the projection for postscan scatter correction. Complementary projections were acquired with grids at complementary locations and were merged to form complete projections for reconstruction. Experiments were conducted with different phantom sizes, grid blocking ratios, image acquisition modes, and reconstruction algorithms to investigate their effects on the scatter reduction and correction. The image quality improvement by the prepatient grids was evaluated both qualitatively through the artifact reduction and quantitatively through contrast-to-noise ratio, Hounsfield-unit accuracy, and uniformity using a CATphan 504 phantom.
RESULTS: Scatter artifacts were reduced by scatter reduction and were removed by scatter correction method. Contrast-to-noise ratio, Hounsfield-unit accuracy, and image uniformity were improved substantially. The simultaneous acquisition mode achieved comparable contrast-to-noise ratio as the interleaved and sequential modes after scatter reduction and correction. Higher grid blocking ratio and smaller phantom size led to higher contrast-to-noise ratio for the simultaneous mode. The iterative reconstruction with total variation regularization was more effective than the Feldkamp, Davis, and Kress method in reducing noise caused by the scatter correction to enhance contrast-to-noise ratio.
CONCLUSION: The prepatient grid system is effective in removing the scatter effects in the simultaneous acquisition mode of the dual-source cone-beam computed tomography, which is useful for scanning time reduction or dual energy imaging.
© The Author(s) 2015.

Entities:  

Keywords:  CNR enhancement; cross scatter; dual energy imaging; grid blocking ratio; scatter artifact; simultaneous acquisition; total variation regularization

Mesh:

Year:  2015        PMID: 26009495      PMCID: PMC4658322          DOI: 10.1177/1533034615587615

Source DB:  PubMed          Journal:  Technol Cancer Res Treat        ISSN: 1533-0338


  29 in total

1.  Optimization of x-ray imaging geometry (with specific application to flat-panel cone-beam computed tomography).

Authors:  J H Siewerdsen; D A Jaffray
Journal:  Med Phys       Date:  2000-08       Impact factor: 4.071

2.  Truncation artifact on PET/CT: impact on measurements of activity concentration and assessment of a correction algorithm.

Authors:  Osama Mawlawi; Jeremy J Erasmus; Tinsu Pan; Dianna D Cody; Rachelle Campbell; Albert H Lonn; Steve Kohlmyer; Homer A Macapinlac; Donald A Podoloff
Journal:  AJR Am J Roentgenol       Date:  2006-05       Impact factor: 3.959

Review 3.  Computational challenges for image-guided radiation therapy: framework and current research.

Authors:  Lei Xing; Jeffrey Siebers; Paul Keall
Journal:  Semin Radiat Oncol       Date:  2007-10       Impact factor: 5.934

4.  Noise suppression in scatter correction for cone-beam CT.

Authors:  Lei Zhu; Jing Wang; Lei Xing
Journal:  Med Phys       Date:  2009-03       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.  Towards the clinical implementation of iterative low-dose cone-beam CT reconstruction in image-guided radiation therapy: cone/ring artifact correction and multiple GPU implementation.

Authors:  Hao Yan; Xiaoyu Wang; Feng Shi; Ti Bai; Michael Folkerts; Laura Cervino; Steve B Jiang; Xun Jia
Journal:  Med Phys       Date:  2014-11       Impact factor: 4.071

7.  Interleaved acquisition for cross scatter avoidance in dual cone-beam CT.

Authors:  William Giles; James Bowsher; Hao Li; Fang-Fang Yin
Journal:  Med Phys       Date:  2012-12       Impact factor: 4.071

8.  Improved image quality of cone beam CT scans for radiotherapy image guidance using fiber-interspaced antiscatter grid.

Authors:  Uros Stankovic; Marcel van Herk; Lennert S Ploeger; Jan-Jakob Sonke
Journal:  Med Phys       Date:  2014-06       Impact factor: 4.071

9.  Dual-energy x-ray projection imaging: two sampling schemes for the correction of scattered radiation.

Authors:  F C Wagner; A Macovski; D G Nishimura
Journal:  Med Phys       Date:  1988 Sep-Oct       Impact factor: 4.071

10.  Compensators for dose and scatter management in cone-beam computed tomography.

Authors:  S A Graham; D J Moseley; J H Siewerdsen; D A Jaffray
Journal:  Med Phys       Date:  2007-07       Impact factor: 4.071

View more
  10 in total

1.  Estimating 4D-CBCT from prior information and extremely limited angle projections using structural PCA and weighted free-form deformation for lung radiotherapy.

Authors:  Wendy Harris; You Zhang; Fang-Fang Yin; Lei Ren
Journal:  Med Phys       Date:  2017-03       Impact factor: 4.071

2.  Reducing scan angle using adaptive prior knowledge for a limited-angle intrafraction verification (LIVE) system for conformal arc radiotherapy.

Authors:  Yawei Zhang; Fang-Fang Yin; You Zhang; Lei Ren
Journal:  Phys Med Biol       Date:  2017-03-24       Impact factor: 3.609

3.  A Biomechanical Modeling Guided CBCT Estimation Technique.

Authors:  You Zhang; Joubin Nasehi Tehrani; Jing Wang
Journal:  IEEE Trans Med Imaging       Date:  2016-11-01       Impact factor: 10.048

4.  Automatic liver tumor localization using deep learning-based liver boundary motion estimation and biomechanical modeling (DL-Bio).

Authors:  Hua-Chieh Shao; Xiaokun Huang; Michael R Folkert; Jing Wang; You Zhang
Journal:  Med Phys       Date:  2021-11-19       Impact factor: 4.071

5.  Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling.

Authors:  Hua-Chieh Shao; Jing Wang; Ti Bai; Jaehee Chun; Justin C Park; Steve Jiang; You Zhang
Journal:  Phys Med Biol       Date:  2022-05-24       Impact factor: 4.174

6.  Enhancing liver tumor localization accuracy by prior-knowledge-guided motion modeling and a biomechanical model.

Authors:  You Zhang; Michael R Folkert; Xiaokun Huang; Lei Ren; Jeffrey Meyer; Joubin Nasehi Tehrani; Robert Reynolds; Jing Wang
Journal:  Quant Imaging Med Surg       Date:  2019-07

7.  Low dose cone-beam computed tomography reconstruction via hybrid prior contour based total variation regularization (hybrid-PCTV).

Authors:  Yingxuan Chen; Fang-Fang Yin; Yawei Zhang; You Zhang; Lei Ren
Journal:  Quant Imaging Med Surg       Date:  2019-07

8.  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

Review 9.  Technical Principles of Dual-Energy Cone Beam Computed Tomography and Clinical Applications for Radiation Therapy.

Authors:  Shailaja Sajja; Young Lee; Markus Eriksson; Håkan Nordström; Arjun Sahgal; Masoud Hashemi; James G Mainprize; Mark Ruschin
Journal:  Adv Radiat Oncol       Date:  2019-07-30

10.  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
  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.