Literature DB >> 20463377

Validation of a method for measuring the volumetric breast density from digital mammograms.

O Alonzo-Proulx1, N Packard, J M Boone, A Al-Mayah, K K Brock, S Z Shen, M J Yaffe.   

Abstract

The purpose of this study was to evaluate the performance of an algorithm used to measure the volumetric breast density (VBD) from digital mammograms. The algorithm is based on the calibration of the detector signal versus the thickness and composition of breast-equivalent phantoms. The baseline error in the density from the algorithm was found to be 1.25 +/- 2.3% VBD units (PVBD) when tested against a set of calibration phantoms, of thicknesses 3-8 cm, with compositions equivalent to fibroglandular content (breast density) between 0% and 100% and under x-ray beams between 26 kVp and 32 kVp with a Rh/Rh anode/filter. The algorithm was also tested against images from a dedicated breast computed tomography (CT) scanner acquired on 26 volunteers. The CT images were segmented into regions representing adipose, fibroglandular and skin tissues, and then deformed using a finite-element algorithm to simulate the effects of compression in mammography. The mean volume, VBD and thickness of the compressed breast for these deformed images were respectively 558 cm(3), 23.6% and 62 mm. The displaced CT images were then used to generate simulated digital mammograms, considering the effects of the polychromatic x-ray spectrum, the primary and scattered energy transmitted through the breast, the anti-scatter grid and the detector efficiency. The simulated mammograms were analyzed with the VBD algorithm and compared with the deformed CT volumes. With the Rh/Rh anode filter, the root mean square difference between the VBD from CT and from the algorithm was 2.6 PVBD, and a linear regression between the two gave a slope of 0.992 with an intercept of -1.4 PVBD and a correlation with R(2) = 0.963. The results with the Mo/Mo and Mo/Rh anode/filter were similar.

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Year:  2010        PMID: 20463377      PMCID: PMC3052857          DOI: 10.1088/0031-9155/55/11/003

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


  40 in total

1.  Scatter/primary in mammography: comprehensive results.

Authors:  J M Boone; K K Lindfors; V N Cooper; J A Seibert
Journal:  Med Phys       Date:  2000-10       Impact factor: 4.071

2.  Dedicated breast CT: radiation dose and image quality evaluation.

Authors:  J M Boone; T R Nelson; K K Lindfors; J A Seibert
Journal:  Radiology       Date:  2001-12       Impact factor: 11.105

Review 3.  Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 1. Tissue and related risk factors.

Authors:  John J Heine; Poonam Malhotra
Journal:  Acad Radiol       Date:  2002-03       Impact factor: 3.173

4.  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

5.  A volumetric method for estimation of breast density on digitized screen-film mammograms.

Authors:  Olga Pawluczyk; Bindu J Augustine; Martin J Yaffe; Dan Rico; Jiwei Yang; Gordon E Mawdsley; Norman F Boyd
Journal:  Med Phys       Date:  2003-03       Impact factor: 4.071

6.  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

Review 7.  Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 2. Serial breast tissue change and related temporal influences.

Authors:  John J Heine; Poonam Malhotra
Journal:  Acad Radiol       Date:  2002-03       Impact factor: 3.173

8.  High mammographic breast density and its implications for the early detection of breast cancer.

Authors:  C H van Gils; J D Otten; J H Hendriks; R Holland; H Straatman; A L Verbeek
Journal:  J Med Screen       Date:  1999       Impact factor: 2.136

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.  ImageParser: a tool for finite element generation from three-dimensional medical images.

Authors:  H M Yin; L Z Sun; G Wang; T Yamada; J Wang; M W Vannier
Journal:  Biomed Eng Online       Date:  2004-10-01       Impact factor: 2.819

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  13 in total

1.  An analysis of the mechanical parameters used for finite element compression of a high-resolution 3D breast phantom.

Authors:  Christina M L Hsu; Mark L Palmeri; W Paul Segars; Alexander I Veress; James T Dobbins
Journal:  Med Phys       Date:  2011-10       Impact factor: 4.071

2.  Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging.

Authors:  Said Pertuz; Elizabeth S McDonald; Susan P Weinstein; Emily F Conant; Despina Kontos
Journal:  Radiology       Date:  2015-10-21       Impact factor: 11.105

Review 3.  Research in digital mammography and tomosynthesis at the University of Toronto.

Authors:  Martin J Yaffe
Journal:  Radiol Phys Technol       Date:  2014-06-25

Review 4.  Measurement of breast density with digital breast tomosynthesis--a systematic review.

Authors:  E U Ekpo; M F McEntee
Journal:  Br J Radiol       Date:  2014-08-22       Impact factor: 3.039

5.  Automated mammographic breast density estimation using a fully convolutional network.

Authors:  Juhun Lee; Robert M Nishikawa
Journal:  Med Phys       Date:  2018-02-19       Impact factor: 4.071

6.  Breast density quantification with cone-beam CT: a post-mortem study.

Authors:  Travis Johnson; Huanjun Ding; Huy Q Le; Justin L Ducote; Sabee Molloi
Journal:  Phys Med Biol       Date:  2013-12-07       Impact factor: 3.609

7.  Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation.

Authors:  Brad M Keller; Diane L Nathan; Yan Wang; Yuanjie Zheng; James C Gee; Emily F Conant; Despina Kontos
Journal:  Med Phys       Date:  2012-08       Impact factor: 4.071

Review 8.  A Review on Automatic Mammographic Density and Parenchymal Segmentation.

Authors:  Wenda He; Arne Juette; Erika R E Denton; Arnau Oliver; Robert Martí; Reyer Zwiggelaar
Journal:  Int J Breast Cancer       Date:  2015-06-11

9.  Mammographic density estimation with automated volumetric breast density measurement.

Authors:  Su Yeon Ko; Eun-Kyung Kim; Min Jung Kim; Hee Jung Moon
Journal:  Korean J Radiol       Date:  2014-04-29       Impact factor: 3.500

10.  Statistical evaluation of a fully automated mammographic breast density algorithm.

Authors:  Mohamed Abdolell; Kaitlyn Tsuruda; Gerry Schaller; Judy Caines
Journal:  Comput Math Methods Med       Date:  2013-05-08       Impact factor: 2.238

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