ArticleContrast media & molecular imaging2017
Automated Generation of Reliable Blood Velocity Parameter Maps from Contrast-Enhanced Ultrasound Data.
Article in Contrast media & molecular imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Motion model ultrasound localization microscopy for preclinical and clinical multiparametric tumor characterization.Nature communications · 2018Article
- Comparison of voiding vesicoureteral urosonography with fluoroscopic voiding cystourethrography in children with vesicoureteral reflux.Pakistan journal of medical sciencesArticle
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Authors and funding
6 authors.
Funding
Abstract
Objectives: The purpose of this study was the automated generation and validation of parametric blood flow velocity maps, based on contrast-enhanced ultrasound (CEUS) scans. Materials and Methods: Ethical approval for animal experiments was obtained. CEUS destruction-replenishment sequences were recorded in phantoms and three different tumor xenograft mouse models. Systematic pixel binning and intensity averaging was performed to generate parameter maps of blood flow velocities with different pixel resolution. The 95% confidence interval of the mean velocity, calculated on the basis of the whole tumor segmentation, served as ground truth for the different parameter maps. Results: In flow phantoms the measured mean velocity values were only weakly influenced by the pixel resolution and correlated with real velocities ( Conclusion: Systematic pixel binning allows the automated identification of optimal pixel resolutions for parametric maps, supporting textural analysis of CEUS data. This approach is independent from the ultrasound setup and can be implemented in the software of other (clinical) ultrasound devices.
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