ArticleMedical & biological engineering & computing2020
Quantitative assessment of microcalcification cluster image quality in digital breast tomosynthesis, 2-dimensional and synthetic mammography.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Automated Segmentation of Mass Regions in DBT Images Using a Dilated DCNN Approach.Computational intelligence and neuroscience · 2022Article
- Fabrication of microcalcifications for insertion into phantoms used to evaluate x-ray breast imaging systems.Biomedical physics & engineering express · 2021Article
- Optimization of Breast Tomosynthesis Visualization through 3D Volume Rendering.Journal of imaging · 2020Article
- Digital Breast Tomosynthesis: Update on Technology, Evidence, and Clinical Practice.Radiographics : a review publication of the Radiological Society of North America, IncArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative assessment of microcalcification (MC) cluster image quality is presented, in terms of cluster signal-difference-to-noise ratio (SDNR) intercomparison among digital breast tomosynthesis (DBT) and 2-dimensional (2D) and synthetic-2-dimensional (s2D) mammography. A phantom that provides realistic appearance of MC clusters located in uniform and nonuniform background was imaged in 2D and DBT, considering various scattering conditions. MC cluster SDNR differentiation is investigated with respect to MC particle size (uniform background) and surrounding parenchyma density (nonuniform background). An accurate MC cluster segmentation method was used to delineate individual MC particles and estimate MC cluster SDNR. Analysis of the uniform part of the phantom indicated higher performance of DBT and 2D over s2D for the smallest cluster size (106-177 μm), no difference among mammographic modes for the largest MC cluster (224-354 μm), and enhanced role of 2D for decreasing cluster size and increasing scattering. Analysis of the nonuniform part of the phantom indicated DBT performed better than 2D and s2D in case of dense parenchyma pattern, while 2D and s2D did not differ across parenchyma density patterns and scattering conditions. The presented MC cluster SDNR analysis was capable of revealing subtle differences among mammographic modes and suggests a methodology for clinical image quality assessment. Graphical abstract.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.