Literature DB >> 21992347

On the orientation of mammographic structure.

I Reiser1, S Lee, R M Nishikawa.   

Abstract

PURPOSE: Burgess et al. have shown that the power-spectral density of mammographic breast tissue P(f) follows a power-law, P(f) = c∕f(β).(1) Due to the complexity of the breast anatomy, breast phantoms often make use of power-law backgrounds to approximate the irregular texture of breast images. However, the current methodology of estimating power-law coefficients assumes that the breast structure is isotropic. The purpose of this letter is to demonstrate that breast anatomic structure is not isotropic, but in fact has a preferred orientation. Further, we present a formalism to estimate power-law coefficients β and c while accounting for tissue orientation in mammographic regions-of-interests (ROIs). We then show the effect of structure orientation on β and c, as well as on the appearance of simulated power-law backgrounds.
METHODS: When breast tissue exhibits a preferred orientation, the radial symmetry in the associated power spectrum is broken. The new symmetry was fit by an ellipsoidal model. Ellipse tilt angle and axis ratio were accounted for in the power-law fit.
RESULTS: On average, breast structure was found to point toward the nipple: the average orientation in MLO views was 22.5 °, while it was 5 ° for CC views, and the mean orientation for left breasts was negative while it was positive for right breasts. For both power-law magnitude and exponent, the mean difference was statistically significant (<Δβ > = -0.096, <Δlog(c) > =-0.192).
CONCLUSIONS: A formalism for quantification of breast structure and structure orientation is provided. The difference in power-law coefficient estimates when accounting for orientation was found to be statistically significant. Examples of statistically defined backgrounds indicate that breast structure is mimicked more closely when structure orientation is accounted for.

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Year:  2011        PMID: 21992347      PMCID: PMC3189972          DOI: 10.1118/1.3633905

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


  11 in total

1.  A three-dimensional breast software phantom for mammography simulation.

Authors:  K Bliznakova; Z Bliznakov; V Bravou; Z Kolitsi; N Pallikarakis
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2.  Quantitative imaging in breast tomosynthesis and CT: comparison of detection and estimation task performance.

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3.  Task-based assessment of breast tomosynthesis: effect of acquisition parameters and quantum noise.

Authors:  I Reiser; R M Nishikawa
Journal:  Med Phys       Date:  2010-04       Impact factor: 4.071

4.  Anatomical background and generalized detectability in tomosynthesis and cone-beam CT.

Authors:  G J Gang; D J Tward; J Lee; J H Siewerdsen
Journal:  Med Phys       Date:  2010-05       Impact factor: 4.071

5.  Signal detection in power-law noise: effect of spectrum exponents.

Authors:  Arthur E Burgess; Philip F Judy
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  2007-12       Impact factor: 2.129

6.  Comparison of power spectra for tomosynthesis projections and reconstructed images.

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7.  Introducing DeBRa: a detailed breast model for radiological studies.

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8.  A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging.

Authors:  Xing Gong; Stephen J Glick; Bob Liu; Aruna A Vedula; Samta Thacker
Journal:  Med Phys       Date:  2006-04       Impact factor: 4.071

9.  Human observer detection experiments with mammograms and power-law noise.

Authors:  A E Burgess; F L Jacobson; P F Judy
Journal:  Med Phys       Date:  2001-04       Impact factor: 4.071

10.  Characterizing anatomical variability in breast CT images.

Authors:  Kathrine G Metheany; Craig K Abbey; Nathan Packard; John M Boone
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  11 in total

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Authors:  Michael D Ketcha; Tharindu De Silva; Runze Han; Ali Uneri; Sebastian Vogt; Gerhard Kleinszig; Jeffrey H Siewerdsen
Journal:  IEEE Trans Med Imaging       Date:  2019-03-27       Impact factor: 10.048

2.  Evaluation of non-Gaussian statistical properties in virtual breast phantoms.

Authors:  Craig K Abbey; Predrag R Bakic; David D Pokrajac; Andrew D A Maidment; Miguel P Eckstein; John M Boone
Journal:  J Med Imaging (Bellingham)       Date:  2019-06-14

3.  Scaling-law for the energy dependence of anatomic power spectrum in dedicated breast CT.

Authors:  Srinivasan Vedantham; Linxi Shi; Stephen J Glick; Andrew Karellas
Journal:  Med Phys       Date:  2013-01       Impact factor: 4.071

4.  Objective models of compressed breast shapes undergoing mammography.

Authors:  Steve Si Jia Feng; Bhavika Patel; Ioannis Sechopoulos
Journal:  Med Phys       Date:  2013-03       Impact factor: 4.071

5.  Effect of slice thickness on detectability in breast CT using a prewhitened matched filter and simulated mass lesions.

Authors:  Nathan J Packard; Craig K Abbey; Kai Yang; John M Boone
Journal:  Med Phys       Date:  2012-04       Impact factor: 4.071

6.  Validation of a power-law noise model for simulating small-scale breast tissue.

Authors:  I Reiser; A Edwards; R M Nishikawa
Journal:  Phys Med Biol       Date:  2013-08-12       Impact factor: 3.609

Review 7.  Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment.

Authors:  Aimilia Gastounioti; Emily F Conant; Despina Kontos
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8.  Human observer performance on in-plane digital breast tomosynthesis images: Effects of reconstruction filters and data acquisition angles on signal detection.

Authors:  Changwoo Lee; Minah Han; Jongduk Baek
Journal:  PLoS One       Date:  2020-03-12       Impact factor: 3.240

9.  Wavelet-based 3D reconstruction of microcalcification clusters from two mammographic views: new evidence that fractal tumors are malignant and Euclidean tumors are benign.

Authors:  Kendra A Batchelder; Aaron B Tanenbaum; Seth Albert; Lyne Guimond; Pierre Kestener; Alain Arneodo; Andre Khalil
Journal:  PLoS One       Date:  2014-09-15       Impact factor: 3.240

10.  Human and model observer performance for lesion detection in breast cone beam CT images with the FDK reconstruction.

Authors:  Minah Han; Byeongjoon Kim; Jongduk Baek
Journal:  PLoS One       Date:  2018-03-15       Impact factor: 3.240

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