Literature DB >> 19841514

Reduction in x-ray scatter and radiation dose for volume-of-interest (VOI) cone-beam breast CT--a phantom study.

Chao-Jen Lai1, Lingyun Chen, Huojun Zhang, Xinming Liu, Yuncheng Zhong, Youtao Shen, Tao Han, Shuaiping Ge, Ying Yi, Tianpeng Wang, Wei T Yang, Gary J Whitman, Chris C Shaw.   

Abstract

With volume-of-interest (VOI) cone-beam computed tomography (CBCT) imaging, one set of projection images are acquired with the VOI collimator at a regular or high exposure level and the second set of projection images are acquired without the collimator at a reduced exposure level. The high exposure VOI scan data inside the VOI and the low exposure full-field scan data outside the VOI are then combined together to generate composite projection images for image reconstruction. To investigate and quantify scatter reduction, dose saving and image quality improvement in VOI CBCT imaging, a flat panel detector-based bench-top experimental CBCT system was built to measure the dose, the scatter-to-primary ratio (SPR), the image contrast, noise level, the contrast-to-noise ratio (CNR) and the figure of merit (FOM) in the CBCT reconstructed images for two polycarbonate cylinders simulating the small and the large phantoms. The results showed that, compared to the full field CBCT technique, radiation doses for the VOI CBCT technique were reduced by a factor of 1.20 and 1.36 for the small and the large phantoms at the phantom center, respectively, and from 2.7 to 3.0 on the edge of the phantom, respectively. Inside the VOI, the SPRs were substantially reduced by a factor of 6.6 and 10.3 for the small and the large phantoms, the contrast signals were improved by a factor of 1.35 and 1.8, and the noise levels were increased by a factor of 1.27 and 1.6, respectively. As a result, the CNRs were improved by a factor of 1.06 and 1.13 for the small and the large phantoms and the FOM improved by a factor of 1.4 and 1.7, respectively.

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Year:  2009        PMID: 19841514      PMCID: PMC2855392          DOI: 10.1088/0031-9155/54/21/016

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


  20 in total

1.  Correction of scatter in megavoltage cone-beam CT.

Authors:  L Spies; M Ebert; B A Groh; B M Hesse; T Bortfeld
Journal:  Phys Med Biol       Date:  2001-03       Impact factor: 3.609

2.  Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter.

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

3.  Effect of scattered radiation on image noise in cone beam CT.

Authors:  M Endo; T Tsunoo; N Nakamori; K Yoshida
Journal:  Med Phys       Date:  2001-04       Impact factor: 4.071

4.  X-ray scatter correction algorithm for cone beam CT imaging.

Authors:  Ruola Ning; Xiangyang Tang; David Conover
Journal:  Med Phys       Date:  2004-05       Impact factor: 4.071

5.  Microcalcification detection using cone-beam CT mammography with a flat-panel imager.

Authors:  Xing Gong; Aruna A Vedula; Stephen J Glick
Journal:  Phys Med Biol       Date:  2004-06-07       Impact factor: 3.609

6.  A comprehensive analysis of DgN(CT) coefficients for pendant-geometry cone-beam breast computed tomography.

Authors:  J M Boone; N Shah; T R Nelson
Journal:  Med Phys       Date:  2004-02       Impact factor: 4.071

7.  Evaluation of x-ray scatter properties in a dedicated cone-beam breast CT scanner.

Authors:  Alexander L C Kwan; John M Boone; Nikula Shah
Journal:  Med Phys       Date:  2005-09       Impact factor: 4.071

8.  An Accurate Scatter Measurement and Correction Technique for Cone Beam Breast CT Imaging Using Scanning Sampled Measurement (SSM) Technique.

Authors:  Xinming Liu; Chris C Shaw; Tianpeng Wang; Lingyun Chen; Mustafa C Altunbas; S Cheenu Kappadath
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2006-02-28

Review 9.  The false-negative mammogram.

Authors:  P T Huynh; A M Jarolimek; S Daye
Journal:  Radiographics       Date:  1998 Sep-Oct       Impact factor: 5.333

10.  Clinical comparison of full-field digital mammography and screen-film mammography for detection of breast cancer.

Authors:  John M Lewin; Carl J D'Orsi; R Edward Hendrick; Lawrence J Moss; Pamela K Isaacs; Andrew Karellas; Gary R Cutter
Journal:  AJR Am J Roentgenol       Date:  2002-09       Impact factor: 3.959

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  7 in total

1.  High resolution dual detector volume-of-interest cone beam breast CT--Demonstration with a bench top system.

Authors:  Youtao Shen; Ying Yi; Yuncheng Zhong; Chao-Jen Lai; Xinming Liu; Zhicheng You; Shuaiping Ge; Tianpeng Wang; Chris C Shaw
Journal:  Med Phys       Date:  2011-12       Impact factor: 4.071

2.  Volume-of-interest imaging of the inner ear in a human temporal bone specimen using a robot- driven C-arm flat panel detector CT system.

Authors:  D Kolditz; T Struffert; Y Kyriakou; A Bozzato; A Dörfler; W A Kalender
Journal:  AJNR Am J Neuroradiol       Date:  2011-08-11       Impact factor: 3.825

3.  Characterization of X-ray scattering for various phantoms and clinical breast geometries using breast CT on a dedicated hybrid system.

Authors:  Jainil P Shah; Steve D Mann; Martin P Tornai
Journal:  J Xray Sci Technol       Date:  2017       Impact factor: 1.535

4.  Radiation doses in volume-of-interest breast computed tomography--A Monte Carlo simulation study.

Authors:  Chao-Jen Lai; Yuncheng Zhong; Ying Yi; Tianpeng Wang; Chris C Shaw
Journal:  Med Phys       Date:  2015-06       Impact factor: 4.071

5.  Reconstruction of a cone-beam CT image via forward iterative projection matching.

Authors:  R Scott Brock; Alen Docef; Martin J Murphy
Journal:  Med Phys       Date:  2010-12       Impact factor: 4.071

6.  CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy.

Authors:  Yingzi Liu; Yang Lei; Tonghe Wang; Yabo Fu; Xiangyang Tang; Walter J Curran; Tian Liu; Pretesh Patel; Xiaofeng Yang
Journal:  Med Phys       Date:  2020-03-28       Impact factor: 4.071

7.  Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum-likelihood polynomial fitting approach.

Authors:  Peymon Ghazi; Andrew M Hernandez; Craig Abbey; Kai Yang; John M Boone
Journal:  Med Phys       Date:  2019-06-23       Impact factor: 4.071

  7 in total

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