Literature DB >> 12201434

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

J Kaufhold1, J A Thomas, J W Eberhard, C E Galbo, D E González Trotter.   

Abstract

The healthy breast is almost entirely composed of a mixture of fatty, epithelial, and stromal tissues which can be grouped into two distinctly attenuating tissue types: fatty and glandular. Further, the amount of glandular tissue is linked to breast cancer risk, so an objective quantitative analysis of glandular tissue can aid in risk estimation. Highnam and Brady have measured glandular tissue composition objectively. However, they argue that their work should only be used for "relative" tissue measurements unless a careful calibration has been performed. In this work, we perform such a "careful calibration" on a digital mammography system and use it to estimate breast tissue composition of patient breasts. We imaged 0%, 50%, and 100% glandular-equivalent phantoms of varying thicknesses for a number of clinically relevant x-ray techniques on a digital mammography system. From these images, we extracted mean signal and noise levels and computed calibration curves that can be used for quantitative tissue composition estimation. In this way, we calculate the percent glandular composition of a patient breast on a pixelwise basis. This tissue composition estimation method was applied to 23 digital mammograms. We estimated the quantitative impact of different error sources on the estimates of tissue composition. These error sources include compressed breast height estimation error, residual scattered radiation, quantum noise, and beam hardening. Errors in the compressed breast height estimate contribute the most error in tissue composition--on the order of +/-7% for a 4 cm compressed breast height: The spatially varying scattered radiation will contribute quantitatively less error overall, but may be significant in regions near the skinline. It is calculated that for a 4 cm compressed breast height, a residual scatter signal error is mitigated by approximately sixfold in the composition estimate. The error in composition due to the quantum noise, which is the limiting noise source in the system, is shown to be less than 1% glandular for most breasts.

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Year:  2002        PMID: 12201434     DOI: 10.1118/1.1493215

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


  37 in total

Review 1.  Clinical and epidemiological issues in mammographic density.

Authors:  Valentina Assi; Jane Warwick; Jack Cuzick; Stephen W Duffy
Journal:  Nat Rev Clin Oncol       Date:  2011-12-06       Impact factor: 66.675

2.  A novel automated mammographic density measure and breast cancer risk.

Authors:  John J Heine; Christopher G Scott; Thomas A Sellers; Kathleen R Brandt; Daniel J Serie; Fang-Fang Wu; Marilyn J Morton; Beth A Schueler; Fergus J Couch; Janet E Olson; V Shane Pankratz; Celine M Vachon
Journal:  J Natl Cancer Inst       Date:  2012-07-03       Impact factor: 13.506

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

Authors:  C J Kotre
Journal:  Br J Radiol       Date:  2010-06       Impact factor: 3.039

4.  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

5.  Scatter radiation in digital tomosynthesis of the breast.

Authors:  Ioannis Sechopoulos; Sankararaman Suryanarayanan; Srinivasan Vedantham; Carl J D'Orsi; Andrew Karellas
Journal:  Med Phys       Date:  2007-02       Impact factor: 4.071

6.  Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit.

Authors:  Lee-Jane W Lu; Thomas K Nishino; Tuenchit Khamapirad; James J Grady; Morton H Leonard; Donald G Brunder
Journal:  Phys Med Biol       Date:  2007-07-30       Impact factor: 3.609

7.  Quantification of breast density with dual energy mammography: a simulation study.

Authors:  Justin L Ducote; Sabee Molloi
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

8.  Classification of breast computed tomography data.

Authors:  Thomas R Nelson; Laura I Cerviño; John M Boone; Karen K Lindfors
Journal:  Med Phys       Date:  2008-03       Impact factor: 4.071

9.  Breast density estimation from high spectral and spatial resolution MRI.

Authors:  Hui Li; William A Weiss; Milica Medved; Hiroyuki Abe; Gillian M Newstead; Gregory S Karczmar; Maryellen L Giger
Journal:  J Med Imaging (Bellingham)       Date:  2016-12-28

10.  An automated approach for estimation of breast density.

Authors:  John J Heine; Michael J Carston; Christopher G Scott; Kathleen R Brandt; Fang-Fang Wu; Vernon Shane Pankratz; Thomas A Sellers; Celine M Vachon
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2008-11       Impact factor: 4.254

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