Literature DB >> 34026921

Validation of a mammographic image quality modification algorithm using 3D-printed breast phantoms.

Joana Boita1,2, Alistair Mackenzie3, Ruben E van Engen2, Mireille Broeders2,4, Ioannis Sechopoulos1,2.   

Abstract

Purpose: To validate a previously proposed algorithm that modifies a mammogram to appear as if it was acquired with different technique factors using realistic phantom-based mammograms. Approach: Two digital mammography systems (an indirect- and a direct-detector-based system) were used to acquire realistic mammographic images of five 3D-printed breast phantoms with the technique factors selected by the automatic exposure control and at various other conditions (denoted by the original images). Additional images under other simulated conditions were also acquired: higher or lower tube voltages, different anode/filter combinations, or lower tube current-time products (target images). The signal and noise in the original images were modified to simulate the target images (simulated images). The accuracy of the image modification algorithm was validated by comparing the target and simulated images using the local mean, local standard deviation (SD), local variance, and power spectra (PS) of the image signals. The absolute relative percent error between the target and simulated images for each parameter was calculated at each sub-region of interest (local parameters) and frequency (PS), and then averaged.
Results: The local mean signal, local SD, local variance, and PS of the target and simulated images were very similar, with a relative percent error of 5.5%, 3.8%, 7.8%, and 4.4% (indirect system), respectively, and of 3.7%, 3.8%, 7.7%, and 7.5% (direct system), respectively. Conclusions: The algorithm is appropriate for simulating different technique factors. Therefore, it can be used in various studies, for instance to evaluate the impact of technique factors in cancer detection using clinical images.
© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).

Entities:  

Keywords:  digital mammography; image quality; image simulation; virtual clinical trials

Year:  2021        PMID: 34026921      PMCID: PMC8134780          DOI: 10.1117/1.JMI.8.3.033502

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


  31 in total

Review 1.  How many observers are needed in clinical studies of medical imaging?

Authors:  Nancy A Obuchowski
Journal:  AJR Am J Roentgenol       Date:  2004-04       Impact factor: 3.959

2.  Method of simulating dose reduction for digital radiographic systems.

Authors:  Magnus Båth; Markus Håkansson; Anders Tingberg; Lars Gunnar Månsson
Journal:  Radiat Prot Dosimetry       Date:  2005       Impact factor: 0.972

3.  Intercomparison of methods for image quality characterization. II. Noise power spectrum.

Authors:  James T Dobbins; Ehsan Samei; Nicole T Ranger; Ying Chen
Journal:  Med Phys       Date:  2006-05       Impact factor: 4.071

4.  Comparative performance of modern digital mammography systems in a large breast screening program.

Authors:  Martin J Yaffe; Aili K Bloomquist; David M Hunter; Gordon E Mawdsley; Anna M Chiarelli; Derek Muradali; James G Mainprize
Journal:  Med Phys       Date:  2013-12       Impact factor: 4.071

5.  The threshold detectable mass diameter for 2D-mammography and digital breast tomosynthesis.

Authors:  Andria Hadjipanteli; Premkumar Elangovan; Alistair Mackenzie; Kevin Wells; David R Dance; Kenneth C Young
Journal:  Phys Med       Date:  2018-12-18       Impact factor: 2.685

6.  Development of an anthropomorphic breast phantom.

Authors:  C B Caldwell; M J Yaffe
Journal:  Med Phys       Date:  1990 Mar-Apr       Impact factor: 4.071

7.  Breast cancer detection rates using four different types of mammography detectors.

Authors:  Alistair Mackenzie; Lucy M Warren; Matthew G Wallis; Julie Cooke; Rosalind M Given-Wilson; David R Dance; Dev P Chakraborty; Mark D Halling-Brown; Padraig T Looney; Kenneth C Young
Journal:  Eur Radiol       Date:  2015-06-25       Impact factor: 5.315

8.  Conversion of mammographic images to appear with the noise and sharpness characteristics of a different detector and x-ray system.

Authors:  Alistair Mackenzie; David R Dance; Adam Workman; Mary Yip; Kevin Wells; Kenneth C Young
Journal:  Med Phys       Date:  2012-05       Impact factor: 4.071

9.  Diagnostic performance of digital versus film mammography for breast-cancer screening.

Authors:  Etta D Pisano; Constantine Gatsonis; Edward Hendrick; Martin Yaffe; Janet K Baum; Suddhasatta Acharyya; Emily F Conant; Laurie L Fajardo; Lawrence Bassett; Carl D'Orsi; Roberta Jong; Murray Rebner
Journal:  N Engl J Med       Date:  2005-09-16       Impact factor: 91.245

10.  Effect of image quality on calcification detection in digital mammography.

Authors:  Lucy M Warren; Alistair Mackenzie; Julie Cooke; Rosalind M Given-Wilson; Matthew G Wallis; Dev P Chakraborty; David R Dance; Hilde Bosmans; Kenneth C Young
Journal:  Med Phys       Date:  2012-06       Impact factor: 4.071

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  2 in total

1.  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

Review 2.  3D and 4D Printing in the Fight against Breast Cancer.

Authors:  Sofia Moroni; Luca Casettari; Dimitrios A Lamprou
Journal:  Biosensors (Basel)       Date:  2022-07-26
  2 in total

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