Literature DB >> 28291738

Design and validation of realistic breast models for use in multiple alternative forced choice virtual clinical trials.

Premkumar Elangovan1, Alistair Mackenzie, David R Dance, Kenneth C Young, Victoria Cooke, Louise Wilkinson, Rosalind M Given-Wilson, Matthew G Wallis, Kevin Wells.   

Abstract

A novel method has been developed for generating quasi-realistic voxel phantoms which simulate the compressed breast in mammography and digital breast tomosynthesis (DBT). The models are suitable for use in virtual clinical trials requiring realistic anatomy which use the multiple alternative forced choice (AFC) paradigm and patches from the complete breast image. The breast models are produced by extracting features of breast tissue components from DBT clinical images including skin, adipose and fibro-glandular tissue, blood vessels and Cooper's ligaments. A range of different breast models can then be generated by combining these components. Visual realism was validated using a receiver operating characteristic (ROC) study of patches from simulated images calculated using the breast models and from real patient images. Quantitative analysis was undertaken using fractal dimension and power spectrum analysis. The average areas under the ROC curves for 2D and DBT images were 0.51  ±  0.06 and 0.54  ±  0.09 demonstrating that simulated and real images were statistically indistinguishable by expert breast readers (7 observers); errors represented as one standard error of the mean. The average fractal dimensions (2D, DBT) for real and simulated images were (2.72  ±  0.01, 2.75  ±  0.01) and (2.77  ±  0.03, 2.82  ±  0.04) respectively; errors represented as one standard error of the mean. Excellent agreement was found between power spectrum curves of real and simulated images, with average β values (2D, DBT) of (3.10  ±  0.17, 3.21  ±  0.11) and (3.01  ±  0.32, 3.19  ±  0.07) respectively; errors represented as one standard error of the mean. These results demonstrate that radiological images of these breast models realistically represent the complexity of real breast structures and can be used to simulate patches from mammograms and DBT images that are indistinguishable from patches from the corresponding real breast images. The method can generate about 500 radiological patches (~30 mm  ×  30 mm) per day for AFC experiments on a single workstation. This is the first study to quantitatively validate the realism of simulated radiological breast images using direct blinded comparison with real data via the ROC paradigm with expert breast readers.

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Year:  2017        PMID: 28291738     DOI: 10.1088/1361-6560/aa622c

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


  6 in total

1.  Advances in Computational Human Phantoms and Their Applications in Biomedical Engineering - A Topical Review.

Authors:  Wolfgang Kainz; Esra Neufeld; Wesley E Bolch; Christian G Graff; Chan Hyeong Kim; Niels Kuster; Bryn Lloyd; Tina Morrison; Paul Segars; Yeon Soo Yeom; Maria Zankl; X George Xu; Benjamin M W Tsui
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2019-01

2.  Development and evaluation of a method for tumor growth simulation in virtual clinical trials of breast cancer screening.

Authors:  Hanna Tomic; Anna Bjerkén; Gustav Hellgren; Kristin Johnson; Daniel Förnvik; Sophia Zackrisson; Anders Tingberg; Magnus Dustler; Predrag R Bakic
Journal:  J Med Imaging (Bellingham)       Date:  2022-06-06

3.  Development of an algorithm to convert mammographic images to appear as if acquired with different technique factors.

Authors:  Alistair Mackenzie; Joana Boita; David R Dance; Kenneth C Young
Journal:  J Med Imaging (Bellingham)       Date:  2022-06-08

4.  Synthetic breast phantoms from patient based eigenbreasts.

Authors:  Gregory M Sturgeon; Subok Park; William Paul Segars; Joseph Y Lo
Journal:  Med Phys       Date:  2017-10-19       Impact factor: 4.071

Review 5.  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

6.  Computational Breast Anatomy Simulation Using Multi-Scale Perlin Noise.

Authors:  Bruno Barufaldi; Craig K Abbey; Miguel A Lago; Trevor L Vent; Raymond J Acciavatti; Predrag R Bakic; Andrew D A Maidment
Journal:  IEEE Trans Med Imaging       Date:  2021-11-30       Impact factor: 10.048

  6 in total

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