Literature DB >> 17921585

Efficiency of antiscatter grids for flat-detector CT.

Yiannis Kyriakou1, Willi Kalender.   

Abstract

Flat-panel detector CT (FD-CT) scanners offer large volume coverage, but as a consequence are more susceptible to scatter artifacts than standard clinical CT scanners with smaller cone angles. FD-CT scanners can employ antiscatter grids as a scatter rejection technique. We evaluated three standard fluoroscopic antiscatter grids for two different field sizes with respect to scatter suppression efficiency and image quality improvement. The evaluations included simulations and measurements. Regarding the simulation a hybrid model combining deterministic and Monte Carlo (MC) calculations was used combined with an analytical calculation of grid transmission. The scatter-to-primary ratio (SPR) was measured using an adapted collimator technique in order to validate our simulations. The SPR obtained by simulations and measurements with and without antiscatter grids were in agreement typically within 10%. The employment of a grid does not generally provide a significant improvement of the signal-to-noise ratio (SNR). Antiscatter grids led to a significant reduction of cupping artifacts in all cases. There is a trade-off between the SNR and the reduction of the scatter intensity described by the signal-to-noise improvement factor (SNR(if)). For low- or medium-scatter conditions the increase in noise caused by the reduced primary transmission through the grid has to be compensated by a higher exposure. For high scatter conditions SNR(if) is significantly greater than 1; i.e. a decrease of dose of up to 50% can be reached.

Mesh:

Year:  2007        PMID: 17921585     DOI: 10.1088/0031-9155/52/20/013

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  13 in total

1.  Monte Carlo evaluation of scatter mitigation strategies in cone-beam CT.

Authors:  Dimitrios Lazos; Jeffrey F Williamson
Journal:  Med Phys       Date:  2010-10       Impact factor: 4.071

2.  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

3.  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

4.  Correction for patient table-induced scattered radiation in cone-beam computed tomography (CBCT).

Authors:  Mingshan Sun; Tamás Nagy; Gary Virshup; Larry Partain; Markus Oelhafen; Josh Star-Lack
Journal:  Med Phys       Date:  2011-04       Impact factor: 4.071

5.  Monte Carlo study of the effects of system geometry and antiscatter grids on cone-beam CT scatter distributions.

Authors:  A Sisniega; W Zbijewski; A Badal; I S Kyprianou; J W Stayman; J J Vaquero; J H Siewerdsen
Journal:  Med Phys       Date:  2013-05       Impact factor: 4.071

6.  Two-dimensional antiscatter grid: A novel scatter rejection device for Cone-beam computed tomography.

Authors:  Timur Alexeev; Brian Kavanagh; Moyed Miften; Cem Altunbas
Journal:  Med Phys       Date:  2018-01-08       Impact factor: 4.071

7.  Learning-based CBCT correction using alternating random forest based on auto-context model.

Authors:  Yang Lei; Xiangyang Tang; Kristin Higgins; Jolinta Lin; Jiwoong Jeong; Tian Liu; Anees Dhabaan; Tonghe Wang; Xue Dong; Robert Press; Walter J Curran; Xiaofeng Yang
Journal:  Med Phys       Date:  2018-12-11       Impact factor: 4.071

8.  Transmission characteristics of a two dimensional antiscatter grid prototype for CBCT.

Authors:  Cem Altunbas; Brian Kavanagh; Timur Alexeev; Moyed Miften
Journal:  Med Phys       Date:  2017-06-16       Impact factor: 4.071

9.  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

10.  Compressed sensing inspired image reconstruction from overlapped projections.

Authors:  Lin Yang; Yang Lu; Ge Wang
Journal:  Int J Biomed Imaging       Date:  2010-06-22
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