Literature DB >> 20505033

X-ray absorptiometry of the breast using mammographic exposure factors: application to units featuring automatic beam quality selection.

C J Kotre1.   

Abstract

A number of studies have identified the relationship between the visual appearance of high breast density at mammography and an increased risk of breast cancer. Approaches to quantify the amount of glandular tissue within the breast from mammography have so far concentrated on image-based methods. Here, it is proposed that the X-ray parameters automatically selected by the mammography unit can be used to estimate the thickness of glandular tissue overlying the automatic exposure sensor area, provided that the unit can be appropriately calibrated. This is a non-trivial task for modern mammography units that feature automatic beam quality selection, as the number of tube potential and X-ray target/filter combinations used to cover the range of breast sizes and compositions can be large, leading to a potentially unworkable number of curve fits and interpolations. Using appropriate models for the attenuation of the glandular breast in conjunction with a constrained set of physical phantom measurements, it is demonstrated that calibration for X-ray absorptiometry can be achieved despite the large number of possible exposure factor combinations employed by modern mammography units. The main source of error on the estimated glandular tissue thickness using this method is shown to be uncertainty in the measured compressed breast thickness. An additional correction for this source of error is investigated and applied. Initial surveys of glandular thickness for a cohort of women undergoing breast screening are presented.

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Year:  2010        PMID: 20505033      PMCID: PMC3473600          DOI: 10.1259/bjr/68799159

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  18 in total

1.  Measurement of breast density with dual X-ray absorptiometry: feasibility.

Authors:  John A Shepherd; Karla M Kerlikowske; Rebecca Smith-Bindman; Harry K Genant; Steve R Cummings
Journal:  Radiology       Date:  2002-05       Impact factor: 11.105

2.  Calculation of dose and contrast for two mammographic grids.

Authors:  D R Dance; J Persliden; G A Carlsson
Journal:  Phys Med Biol       Date:  1992-01       Impact factor: 3.609

3.  Automated analysis of mammographic densities and breast carcinoma risk.

Authors:  J W Byng; M J Yaffe; G A Lockwood; L E Little; D L Tritchler; N F Boyd
Journal:  Cancer       Date:  1997-07-01       Impact factor: 6.860

4.  Dosimetric implications of age related glandular changes in screening mammography.

Authors:  J R Beckett; C J Kotre
Journal:  Phys Med Biol       Date:  2000-03       Impact factor: 3.609

5.  A method for estimating compressed breast thickness during mammography.

Authors:  A Burch; J Law
Journal:  Br J Radiol       Date:  1995-04       Impact factor: 3.039

6.  Absorbed radiation dose in mammography.

Authors:  G R Hammerstein; D W Miller; D R White; M E Masterson; H Q Woodard; J S Laughlin
Journal:  Radiology       Date:  1979-02       Impact factor: 11.105

7.  Risk for breast cancer development determined by mammographic parenchymal pattern.

Authors:  J N Wolfe
Journal:  Cancer       Date:  1976-05       Impact factor: 6.860

8.  Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study.

Authors:  N F Boyd; J W Byng; R A Jong; E K Fishell; L E Little; A B Miller; G A Lockwood; D L Tritchler; M J Yaffe
Journal:  J Natl Cancer Inst       Date:  1995-05-03       Impact factor: 13.506

9.  A calibration approach to glandular tissue composition estimation in digital mammography.

Authors:  J Kaufhold; J A Thomas; J W Eberhard; C E Galbo; D E González Trotter
Journal:  Med Phys       Date:  2002-08       Impact factor: 4.071

10.  Mammographic features and breast cancer risk: effects with time, age, and menopause status.

Authors:  C Byrne; C Schairer; J Wolfe; N Parekh; M Salane; L A Brinton; R Hoover; R Haile
Journal:  J Natl Cancer Inst       Date:  1995-11-01       Impact factor: 13.506

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  1 in total

1.  Statistical analysis of mammographic breast composition measurements: towards a quantitative measure of relative breast cancer risk.

Authors:  C J Kotre
Journal:  Br J Radiol       Date:  2010-11-16       Impact factor: 3.039

  1 in total

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