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

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.

[1]  C B Caldwell,et al.  Development of an anthropomorphic breast phantom. , 1990, Medical physics.

[2]  Kyle J. Myers,et al.  Virtual Tools for the Evaluation of Breast Imaging: State-of-the Science and Future Directions , 2016, Digital Mammography / IWDM.

[3]  D. DeLong,et al.  Digital mammography: effects of reduced radiation dose on diagnostic performance. , 2007, Radiology.

[4]  Ann-Katherine Carton,et al.  The effect of scatter and glare on image quality in contrast-enhanced breast imaging using an a-Si/CsI(TI) full-field flat panel detector. , 2009, Medical physics.

[5]  Dev P Chakraborty,et al.  The relationship between cancer detection in mammography and image quality measurements. , 2016, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[6]  Wei-Chung Cheng,et al.  A four-alternative forced choice (4AFC) methodology for evaluating microcalcification detection in clinical full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) systems using an inkjet-printed anthropomorphic phantom. , 2019, Medical physics.

[7]  E. Samei,et al.  Dose dependence of mass and microcalcification detection in digital mammography: free response human observer studies. , 2007, Medical physics.

[8]  Kenneth C. Young,et al.  Sampling probability distributions of lesions in mammograms , 2015, Medical Imaging.

[9]  Ioannis Sechopoulos,et al.  Breast phantom validation of a mammographic image modification method , 2018, Other Conferences.

[10]  Oliver Diaz,et al.  Image simulation and a model of noise power spectra across a range of mammographic beam qualities. , 2014, Medical physics.

[11]  S. Feig,et al.  Image quality of screening mammography: effect on clinical outcome. , 2002, AJR. American journal of roentgenology.

[12]  Anders Tingberg,et al.  Method of simulating dose reduction for digital radiographic systems. , 2005, Radiation protection dosimetry.

[13]  Ehsan Samei,et al.  A method for modifying the image quality parameters of digital radiographic images. , 2003, Medical physics.

[14]  Hilde Bosmans,et al.  Effect of image quality on calcification detection in digital mammography. , 2012, Medical physics.

[15]  N. Obuchowski How many observers are needed in clinical studies of medical imaging? , 2004, AJR. American journal of roentgenology.

[16]  H. Bosmans,et al.  Impact of compressed breast thickness and dose on lesion detectability in digital mammography: FROC study with simulated lesions in real mammograms. , 2016, Medical physics.

[17]  Premkumar Elangovan,et al.  The threshold detectable mass diameter for 2D-mammography and digital breast tomosynthesis. , 2019, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[18]  M Ruschin,et al.  Visibility of microcalcification clusters and masses in breast tomosynthesis image volumes and digital mammography: a 4AFC human observer study. , 2012, Medical physics.

[19]  Kenneth C. Young,et al.  A method to modify mammography images to a appear as if acquired using different radiographic factors , 2019, Medical Imaging.

[20]  Kenneth C. Young,et al.  Conversion of mammographic images to appear with the noise and sharpness characteristics of a different detector and x-ray system. , 2012, Medical physics.

[21]  Hilde Bosmans,et al.  Evaluation of clinical image processing algorithms used in digital mammography. , 2009, Medical physics.

[22]  N W Marshall,et al.  Retrospective analysis of a detector fault for a full field digital mammography system , 2006, Physics in medicine and biology.

[23]  N A Obuchowski,et al.  Data analysis for detection and localization of multiple abnormalities with application to mammography. , 2000, Academic radiology.

[24]  Julie Cooke,et al.  Breast cancer detection rates using four different types of mammography detectors , 2016, European Radiology.

[25]  Andrew D. A. Maidment,et al.  Conditioning data for calculation of the modulation transfer function. , 2003, Medical physics.

[26]  Andreu Badal,et al.  Reproducing two-dimensional mammograms with three-dimensional printed phantoms , 2018, Journal of medical imaging.

[27]  Magnus Båth,et al.  Simulation of dose reduction in tomosynthesis. , 2009, Medical physics.

[28]  Ioannis Sechopoulos,et al.  Validation of a method to simulate the acquisition of mammographic images with different techniques , 2019, Medical Imaging.

[29]  Ying Chen,et al.  Intercomparison of methods for image quality characterization. II. Noise power spectrum. , 2006, Medical physics.

[30]  C. D'Orsi,et al.  Diagnostic Performance of Digital Versus Film Mammography for Breast-Cancer Screening , 2005, The New England journal of medicine.

[31]  Martin Fiebich,et al.  Breast phantoms for 2D digital mammography with realistic anatomical structures and attenuation characteristics based on clinical images using 3D printing , 2019, Physics in medicine and biology.

[32]  Dev P Chakraborty,et al.  Observer studies involving detection and localization: modeling, analysis, and validation. , 2004, Medical physics.

[33]  Arthur E. Burgess Effect of detector element size on signal detectability in digital mammography , 2005, SPIE Medical Imaging.

[34]  Charles E Metz,et al.  Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems. , 2006, Journal of the American College of Radiology : JACR.

[35]  M. Yaffe,et al.  Comparative performance of modern digital mammography systems in a large breast screening program. , 2013, Medical physics.

[36]  J Law The commissioning and routine testing of mammographic X-ray systems : a protocol produced by a working party of the Diagnostic Radiology Topic Group, Institute of Physical Sciences in Medicine , 1994 .

[37]  Stephen J Glick,et al.  Advances in digital and physical anthropomorphic breast phantoms for x-ray imaging. , 2018, Medical physics.

[38]  Ehsan Samei,et al.  Does image quality matter? Impact of resolution and noise on mammographic task performance. , 2007, Medical physics.