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.

PURPOSE The advent of 3D breast imaging systems such as digital breast tomosynthesis (DBT) has great promise for improving the detection and diagnosis of breast cancer. With these new technologies comes an essential need for testing methods to assess the resultant image quality. Although randomized clinical trials are the gold-standard for assessing image quality, phantom-based studies can provide a simpler and less burdensome approach. In this work, a complete framework is presented for task-based evaluation of microcalcification (MCs) detection performance for DBT imaging systems. METHODS The framework consists of three parts. The first part is a realistic anthropomorphic physical breast phantom created through inkjet printing, with parchment paper and iodine-doped ink. The second is a method for inserting realistic MCs fabricated from calcium hydroxyapatite. The reproducibility and stability of the phantom materials were investigated through multiple samples of parchment and ink over six months. The final part is an analysis using a four alternative forced choice (4AFC) reader study. To demonstrate the framework, a task-based 4AFC study was conducted using a clinical system to compare performance from DBT, synthetic mammography (SM), and full field digital mammography (FFDM). Nine human observers read images containing MC clusters imaged with all three modalities and tried to correctly locate the MCs. The proportion correct (PC) was measured as the number of correctly detected clusters out of all trials. RESULTS Overall, readers scored the highest with FFDM, (PC = 0.95 ± 0.03) then DBT (0.85 ± 0.04), and finally SM (0.44 ± 0.06). For the parchment and ink samples, the linear attenuation properties were very stable over six months. In addition, little difference was found between the various parchment and ink samples, indicating good reproducibility. CONCLUSIONS This framework presents a promising methodology for evaluating diagnostic task performance of clinical breast DBT systems. This article is protected by copyright. All rights reserved.

[1]  Christian G. Graff,et al.  A new, open-source, multi-modality digital breast phantom , 2016, SPIE Medical Imaging.

[2]  Joseph Y. Lo,et al.  Methodology for the objective assessment of lesion detection performance with breast tomosynthesis and digital mammography using a physical anthropomorphic phantom , 2018, Medical Imaging.

[3]  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.

[4]  Nancy A Obuchowski,et al.  Multi-reader ROC studies with split-plot designs: a comparison of statistical methods. , 2012, Academic radiology.

[5]  Andrew D. A. Maidment,et al.  OpenVCT: a GPU-accelerated virtual clinical trial pipeline for mammography and digital breast tomosynthesis , 2018, Medical Imaging.

[6]  Jered R. Wells,et al.  TU‐CD‐207‐08: Intrinsic Image Quality Comparison of Synthesized 2‐D and FFDM Images , 2015 .

[7]  Frank W. Samuelson,et al.  In silico imaging clinical trials for regulatory evaluation: initial considerations for VICTRE, a demonstration study , 2017, Medical Imaging.

[8]  Jongduk Baek,et al.  Evaluation of human observer performance on lesion detectability in single‐slice and multislice dedicated breast cone beam CT images with breast anatomical background , 2018, Medical physics.

[9]  Rhian Gabe,et al.  The randomized trials of breast cancer screening: what have we learned? , 2004, Radiologic clinics of North America.

[10]  Ehsan Samei,et al.  Assessing task performance in FFDM, DBT, and synthetic mammography using uniform and anthropomorphic physical phantoms. , 2016, Medical physics.

[11]  Heang-Ping Chan,et al.  Digital breast tomosynthesis: observer performance of clustered microcalcification detection on breast phantom images acquired with an experimental system using variable scan angles, angular increments, and number of projection views. , 2014, Radiology.

[12]  Rongping Zeng,et al.  Evaluating the sensitivity of the optimization of acquisition geometry to the choice of reconstruction algorithm in digital breast tomosynthesis through a simulation study. , 2015, Physics in medicine and biology.

[13]  Andreu Badal,et al.  A novel physical anthropomorphic breast phantom for 2D and 3D x‐ray imaging , 2017, Medical physics.

[14]  Hilde Bosmans,et al.  A four-alternative forced choice (4AFC) software for observer performance evaluation in radiology , 2016, SPIE Medical Imaging.

[15]  Ann-Katherine Carton,et al.  Development of a physical 3D anthropomorphic breast phantom. , 2011, Medical physics.

[16]  Erik Fredenberg,et al.  Energy weighting improves dose efficiency in clinical practice: implementation on a spectral photon-counting mammography system , 2014, Journal of medical imaging.

[17]  S S Sagel,et al.  Omental flap in lung transplantation. , 1992, Radiology.

[18]  A. Jemal,et al.  Breast cancer statistics, 2015: Convergence of incidence rates between black and white women , 2016, CA: a cancer journal for clinicians.

[19]  Kenneth C. Young,et al.  Comparison of synthetic 2D images with planar and tomosynthesis imaging of the breast using a virtual clinical trial , 2018, Medical Imaging.

[20]  Daniel B Kopans,et al.  Digital breast tomosynthesis from concept to clinical care. , 2014, AJR. American journal of roentgenology.

[21]  D R Dance,et al.  Comparison of the x-ray attenuation properties of breast calcifications, aluminium, hydroxyapatite and calcium oxalate , 2013, Physics in medicine and biology.

[22]  Hilde Bosmans,et al.  Development and validation of a modelling framework for simulating 2D-mammography and breast tomosynthesis images , 2014, Physics in medicine and biology.

[23]  Andrew D. A. Maidment,et al.  Virtual clinical trial of lesion detection in digital mammography and digital breast tomosynthesis , 2018, Medical Imaging.

[24]  Andrey Makeev,et al.  Development of a physical anthropomorphic breast phantom for objective task-based assessment of dedicated breast CT systems , 2018, Other Conferences.

[25]  Andriy I. Bandos,et al.  Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program. , 2013, Radiology.

[26]  Premkumar Elangovan,et al.  A deep learning model observer for use in alterative forced choice virtual clinical trials , 2018, Medical Imaging.

[27]  Michael Scheel,et al.  Radiopaque Three-dimensional Printing: A Method to Create Realistic CT Phantoms. , 2017, Radiology.

[28]  D. Dance,et al.  Design and application of a structured phantom for detection performance comparison between breast tomosynthesis and digital mammography , 2017, Physics in medicine and biology.

[29]  Andrey Makeev,et al.  Classification of breast microcalcifications using dual-energy mammography , 2019, Journal of medical imaging.

[30]  Andrew Oustimov,et al.  Effectiveness of Digital Breast Tomosynthesis Compared With Digital Mammography: Outcomes Analysis From 3 Years of Breast Cancer Screening. , 2016, JAMA oncology.