Literature DB >> 28715130

Average glandular dose coefficients for pendant-geometry breast CT using realistic breast phantoms.

Andrew M Hernandez1, John M Boone2.   

Abstract

PURPOSE: To design volume-specific breast phantoms from breast CT (bCT) data sets and estimate the associated normalized mean glandular dose coefficients for breast CT using Monte Carlo methods.
METHODS: A large cohort of bCT data sets (N = 215) was used to evaluate breast volume into quintiles (plus the top 5%). The average radius profile was then determined for each of the six volume-specific groups and used to both fabricate physical phantoms and generate mathematical phantoms (V1-V6; "V" denotes classification by volume). The MCNP6 Monte Carlo code was used to model a prototype bCT system fabricated at our institution; and this model was validated against physical measurements in the fabricated phantoms. The mathematical phantoms were used to simulate normalized mean glandular dose coefficients for both monoenergetic source photons "DgNCT (E)" (8-70 keV in 1 keV intervals) and polyenergetic x-ray beams "pDgNCT " (35-70 kV in 1 kV intervals). The Monte Carlo code was used to study the influence of breast size (V1 vs. V5) and glandular fraction (6.4% vs. 45.8%) on glandular dose. The pDgNCT coefficients estimated for the V1, V3, and V5 phantoms were also compared to those generated using simple, cylindrical phantoms with equivalent volume and two geometrical constraints including; (a) cylinder radius determined at the breast phantom chest wall "Rcw "; and (b) cylinder radius determined at the breast phantom center-of-mass "RCOM ".
RESULTS: Satisfactory agreement was observed for dose estimations using MCNP6 compared with both physical measurements in the V1, V3, and V5 phantoms (R2 = 0.995) and reference bCT dose coefficients using simple phantoms (R2 = 0.999). For a 49 kV spectrum with 1.5 mm Al filtration, differences in glandular fraction [6.5% (5th percentile) vs. 45.8% (95th percentile)] had a 13.2% influence on pDgNCT for the V3 phantom, and differences in breast size (V1 vs. V5) had a 16.6% influence on pDgNCT for a breast composed of 17% (median) fibroglandular tissue. For cylindrical phantoms with a radius of RCOM , the differences were 1.5%, 0.1%, and 2.1% compared with the V1, V3, and V5 phantoms, respectively.
CONCLUSION: Breast phantoms were designed using a large cohort of bCT data sets across a range of six breast sizes. These phantoms were then fabricated and used for the estimation of glandular dose in breast CT. The mathematical phantoms and associated glandular dose coefficients for a range of breast sizes (V1-V6) and glandular fractions (5th to 95th percentiles) are available for interested users.
© 2017 American Association of Physicists in Medicine.

Entities:  

Keywords:  breast cancer; breast computed tomography; dosimetry; glandular breast dose; x-ray imaging

Mesh:

Year:  2017        PMID: 28715130      PMCID: PMC5646224          DOI: 10.1002/mp.12477

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


  12 in total

1.  Normalized glandular dose (DgN) coefficients for arbitrary X-ray spectra in mammography: computer-fit values of Monte Carlo derived data.

Authors:  John M Boone
Journal:  Med Phys       Date:  2002-05       Impact factor: 4.071

2.  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

3.  Dosimetric characterization of a dedicated breast computed tomography clinical prototype.

Authors:  Ioannis Sechopoulos; Steve Si Jia Feng; Carl J D'Orsi
Journal:  Med Phys       Date:  2010-08       Impact factor: 4.071

4.  Monte Carlo reference data sets for imaging research: Executive summary of the report of AAPM Research Committee Task Group 195.

Authors:  Ioannis Sechopoulos; Elsayed S M Ali; Andreu Badal; Aldo Badano; John M Boone; Iacovos S Kyprianou; Ernesto Mainegra-Hing; Kyle L McMillan; Michael F McNitt-Gray; D W O Rogers; Ehsan Samei; Adam C Turner
Journal:  Med Phys       Date:  2015-10       Impact factor: 4.071

5.  Breast dose in mammography is about 30% lower when realistic heterogeneous glandular distributions are considered.

Authors:  Andrew M Hernandez; J Anthony Seibert; John M Boone
Journal:  Med Phys       Date:  2015-11       Impact factor: 4.071

6.  Monte Carlo and phantom study of the radiation dose to the body from dedicated CT of the breast.

Authors:  Ioannis Sechopoulos; Srinivasan Vedantham; Sankararaman Suryanarayanan; Carl J D'Orsi; Andrew Karellas
Journal:  Radiology       Date:  2008-02-21       Impact factor: 11.105

7.  The myth of the 50-50 breast.

Authors:  M J Yaffe; J M Boone; N Packard; O Alonzo-Proulx; S Y Huang; C L Peressotti; A Al-Mayah; K Brock
Journal:  Med Phys       Date:  2009-12       Impact factor: 4.071

8.  The effect of skin thickness determined using breast CT on mammographic dosimetry.

Authors:  Shih-Ying Huang; John M Boone; Kai Yang; Alexander L C Kwan; Nathan J Packard
Journal:  Med Phys       Date:  2008-04       Impact factor: 4.071

9.  Evolution of spatial resolution in breast CT at UC Davis.

Authors:  Peymon M Gazi; Kai Yang; George W Burkett; Shadi Aminololama-Shakeri; J Anthony Seibert; John M Boone
Journal:  Med Phys       Date:  2015-04       Impact factor: 4.071

10.  Generation and analysis of clinically relevant breast imaging x-ray spectra.

Authors:  Andrew M Hernandez; J Anthony Seibert; Anita Nosratieh; John M Boone
Journal:  Med Phys       Date:  2017-05-04       Impact factor: 4.071

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  4 in total

1.  High resolution microcalcification signal profiles for dedicated breast CT.

Authors:  Andrew M Hernandez; Amy E Becker; Su Hyun Lyu; Craig K Abbey; John M Boone
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2020-03-16

2.  Effects of kV, filtration, dose, and object size on soft tissue and iodine contrast in dedicated breast CT.

Authors:  Andrew M Hernandez; Craig K Abbey; Peymon Ghazi; George Burkett; John M Boone
Journal:  Med Phys       Date:  2020-04-27       Impact factor: 4.071

3.  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

4.  High-resolution μ CT imaging for characterizing microcalcification detection performance in breast CT.

Authors:  Andrew M Hernandez; Amy E Becker; Su Hyun Lyu; Craig K Abbey; John M Boone
Journal:  J Med Imaging (Bellingham)       Date:  2021-07-20
  4 in total

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