Literature DB >> 27660807

Impact of breast structure on lesion detection in breast tomosynthesis, a simulation study.

Nooshin Kiarashi1, Loren W Nolte2, Joseph Y Lo3, W Paul Segars4, Sujata V Ghate5, Justin B Solomon4, Ehsan Samei6.   

Abstract

This study aims to characterize the effect of background tissue density and heterogeneity on the detection of irregular masses in breast tomosynthesis, while demonstrating the capability of the sophisticated tools that can be used in the design, implementation, and performance analysis of virtual clinical trials (VCTs). Twenty breast phantoms from the extended cardiac-torso (XCAT) family, generated based on dedicated breast computed tomography of human subjects, were used to extract a total of 2173 volumes of interest (VOIs) from simulated tomosynthesis images. Five different lesions, modeled after human subject tomosynthesis images, were embedded in the breasts and combined with the lesion absent condition yielded a total of [Formula: see text] VOIs. Effects of background tissue density and heterogeneity on the detection of the lesions were studied by implementing a composite hypothesis signal detection paradigm with location known exactly, lesion known exactly or statistically, and background known statistically. Using the area under the receiver operating characteristic curve, detection performance deteriorated as density was increased, yielding findings consistent with clinical studies. A human observer study was performed on a subset of the simulated tomosynthesis images, confirming the detection performance trends with respect to density and serving as a validation of the implemented detector. Performance of the implemented detector varied substantially across the 20 breasts. Furthermore, background tissue density and heterogeneity affected the log-likelihood ratio test statistic differently under lesion absent and lesion present conditions. Therefore, considering background tissue variability in tissue models can change the outcomes of a VCT and is hence of crucial importance. The XCAT breast phantoms have the potential to address this concern by offering realistic modeling of background tissue variability based on a wide range of human subjects, comprising various breast shapes, sizes, and densities.

Entities:  

Keywords:  Monte Carlo integration; anthropomorphic lesion models; breast density; breast imaging; detection; digital breast tomosynthesis; doubly composite hypothesis testing; extended cardiac-torso breast phantoms; performance evaluation; receiver operating characteristic curve analysis; tissue heterogeneity; virtual clinical trials

Year:  2016        PMID: 27660807      PMCID: PMC5020162          DOI: 10.1117/1.JMI.3.3.035504

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  16 in total

1.  Impact of resolution and noise characteristics of digital radiographic detectors on the detectability of lung nodules.

Authors:  Robert S Saunders; Ehsan Samei; Christoph Hoeschen
Journal:  Med Phys       Date:  2004-06       Impact factor: 4.071

2.  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

3.  Population of 224 realistic human subject-based computational breast phantoms.

Authors:  David W Erickson; Jered R Wells; Gregory M Sturgeon; Ehsan Samei; James T Dobbins; W Paul Segars; Joseph Y Lo
Journal:  Med Phys       Date:  2016-01       Impact factor: 4.071

4.  A comparison of reconstruction algorithms for breast tomosynthesis.

Authors:  Tao Wu; Richard H Moore; Elizabeth A Rafferty; Daniel B Kopans
Journal:  Med Phys       Date:  2004-09       Impact factor: 4.071

5.  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

6.  Methodology for generating a 3D computerized breast phantom from empirical data.

Authors:  Christina M Li; W Paul Segars; Georgia D Tourassi; John M Boone; James T Dobbins
Journal:  Med Phys       Date:  2009-07       Impact factor: 4.071

7.  Finite-element modeling of compression and gravity on a population of breast phantoms for multimodality imaging simulation.

Authors:  Gregory M Sturgeon; Nooshin Kiarashi; Joseph Y Lo; E Samei; W P Segars
Journal:  Med Phys       Date:  2016-05       Impact factor: 4.071

8.  Effect of age and breast density on screening mammograms with false-positive findings.

Authors:  C D Lehman; E White; S Peacock; M J Drucker; N Urban
Journal:  AJR Am J Roentgenol       Date:  1999-12       Impact factor: 3.959

9.  Breast density as a predictor of mammographic detection: comparison of interval- and screen-detected cancers.

Authors:  M T Mandelson; N Oestreicher; P L Porter; D White; C A Finder; S H Taplin; E White
Journal:  J Natl Cancer Inst       Date:  2000-07-05       Impact factor: 13.506

10.  Quantification of scattered radiation in projection mammography: four practical methods compared.

Authors:  Elena Salvagnini; Hilde Bosmans; Lara Struelens; Nicholas W Marshall
Journal:  Med Phys       Date:  2012-06       Impact factor: 4.071

View more
  3 in total

1.  Virtual assessment of stereoscopic viewing of digital breast tomosynthesis projection images.

Authors:  Gezheng Wen; Ho-Chang Chang; Jacob Reinhold; Joseph Y Lo; Mia K Markey
Journal:  J Med Imaging (Bellingham)       Date:  2018-01-17

2.  Three-dimensionally-printed anthropomorphic physical phantom for mammography and digital breast tomosynthesis with custom materials, lesions, and uniform quality control region.

Authors:  Andrea H Rossman; Matthew Catenacci; Christine Zhao; Dhiraj Sikaria; John E Knudsen; Danielle Dawes; Michael E Gehm; Ehsan Samei; Benjamin J Wiley; Joseph Y Lo
Journal:  J Med Imaging (Bellingham)       Date:  2019-03-22

Review 3.  Virtual clinical trials in medical imaging: a review.

Authors:  Ehsan Abadi; William P Segars; Benjamin M W Tsui; Paul E Kinahan; Nick Bottenus; Alejandro F Frangi; Andrew Maidment; Joseph Lo; Ehsan Samei
Journal:  J Med Imaging (Bellingham)       Date:  2020-04-11
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.