ArticleJournal of medical imaging (Bellingham, Wash.)2019
Evaluation of non-Gaussian statistical properties in virtual breast phantoms.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Frequency-based texture analysis of non-Gaussian properties of digital breast tomosynthesis images and comparison across two vendors.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Iodine quantification performance with deep silicon-based Photon-Counting CT: A virtual imaging trial study.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 (AIFB) · 2025Article
- Toward widespread use of virtual trials in medical imaging innovation and regulatory science.Medical physics · 2024Review
- Local texture statistics augment the power spectrum in modeling radiographic judgments of breast density.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Computational Breast Anatomy Simulation Using Multi-Scale Perlin Noise.IEEE transactions on medical imaging · 2021Article
- Evaluation of non-Gaussian statistical properties in virtual breast phantoms.Journal of medical imaging (Bellingham, Wash.) · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Images derived from a "virtual phantom" can be useful in characterizing the performance of imaging systems. This has driven the development of virtual breast phantoms implemented in simulation environments. In breast imaging, several such phantoms have been proposed. We analyze the non-Gaussian statistical properties from three classes of virtual breast phantoms and compare them to similar statistics from a database of breast images. These include clustered-blob lumpy backgrounds (CBLBs), truncated binary textures, and the UPenn virtual breast phantoms. We use Laplacian fractional entropy (LFE) as a measure of the non-Gaussian statistical properties of each simulation procedure. Our results show that, despite similar power spectra, the simulation approaches differ considerably in LFE with very low scores for the CBLB to high values for the UPenn phantom at certain frequencies. These results suggest that LFE may have value in developing and tuning virtual phantom simulation procedures.
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Registered trials
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