Assessment of task‐based performance from five clinical DBT systems using an anthropomorphic breast phantom

Abstract

Purpose Digital breast tomosynthesis (DBT) is a limited‐angle tomographic breast imaging modality that can be used for breast cancer screening in conjunction with full‐field digital mammography (FFDM) or synthetic mammography (SM). Currently, there are five commercial DBT systems that have been approved by the U.S. FDA for breast cancer screening, all varying greatly in design and imaging protocol. Because the systems are different in technical specifications, there is a need for a quantitative approach for assessing them. In this study, the DBT systems are assessed using a novel methodology with an inkjet‐printed anthropomorphic phantom and four alternative forced choice (4AFC) study scheme. Method A breast phantom was fabricated using inkjet printing and parchment paper. The phantom contained 5‐mm spiculated masses fabricated with potassium iodide (KI)‐doped ink and microcalcifications (MCs) made with calcium hydroxyapatite. Images of the phantom were acquired on all five systems with DBT, FFDM, and SM modalities where available using beam settings under automatic exposure control. A 4AFC study was conducted to assess reader performance with each signal under each modality. Statistical analysis was performed on the data to determine proportion correct (PC), standard deviations, and levels of significance. Results For masses, overall detection was highest with DBT. The difference in PC was statistically significant between DBT and SM for most systems. A relationship was observed between increasing PC and greater gantry span. For MCs, performance was highest with DBT and FFDM compared to SM. The difference between PC of DBT and PC of SM was statistically significant for all manufacturers. Conclusions This methodology represents a novel approach for evaluating systems. This study is the first of its kind to use an inkjet‐printed anthropomorphic phantom with realistic signals to assess performance of clinical DBT imaging systems.

Hyperia: A novel methodology of developing anthropomorphic breast phantoms for X‐ray imaging modalities – Part I: Concept and initial findings

“…This is not a trivial task as a realistic addition of the features requires a 3D localization within the Hyperia phantom. For instance, while other works have included microcalcification inserts in a single layer, [33][34][35] creating a pendant breast phantom requires a 3D placement of the specks layer by layer. A realistic implementation of the microcalcification cluster requires the positioning of the different specs in different layers.…”

Comparison of lesion detection and conspicuity between narrow-angle and wide-angle digital breast tomosynthesis for dense and non-dense breasts

“…Studies have been conducted extensively to investigate the impact of AR on DBT through cascaded linear system modeling, [6][7][8][9] simulation studies, [10][11][12][13][14] physical phantom experiments, 7,11,[15][16][17][18][19][20] and evaluations using model observer [21][22][23][24][25][26][27][28][29][30] and human observer. [30][31][32][33][34][35][36] The impact of AR on the breast structural background in DBT has also been assessed. [37][38][39] Nosratieh et al investigated the slice sensitivity profiles (SSP) for different AR using subsets of projection images from a breast CT (bCT) scanner and showed that SSP improves with increasing AR.…”

“…Studies have been conducted extensively to investigate the impact of AR on DBT through cascaded linear system modeling, 6 – 9 simulation studies, 10 – 14 physical phantom experiments, 7 , 11 , 15 – 20 and evaluations using model observer 21 – 30 and human observer 30 – 36 The impact of AR on the breast structural background in DBT has also been assessed 37 – 39 …”

“…Ikejimba et al. assessed the performance of five clinical DBT systems using an inkjet-printed anthropomorphic phantom with artificial masses and microcalcifications (MCs), and observed that the proportion of correctly detected mass lesions increases with greater AR 36 . These studies indicate that DBT with wider AR may improve the detection of masses for patients with dense breasts.…”

Task-based assessment of digital mammography microcalcification detection with deep learning denoising algorithms using in silico and physical phantom studies

“…The first approach used a previously described physical anthropomorphic breast phantom with inserted microcalcification clusters of small size. 14 Although it is unclear if physical breast phantoms can be used to collect enough training data to train the denoising network, the approach could be used to evaluate networks previously trained on clinical data. The second approach used in silico computational methods with a previously developed anthropomorphic digital breast phantom 15 and GPU accelerated Monte-Carlo software 16 for simulating the imaging acquisition process.…”