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Quantification of Calcium Amount in a New Experimental Model: A Comparison between Ultrasound and Computed Tomography
PURPOSE: Calcification is an important prognostic factor in aortic valve stenosis. However, there is no ultrasound (US) method available to accurately quantify calcification in this setting to date. We aimed to validate a new US method for measuring the amount of calcium in an in vitro model, and co...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4747484/ https://www.ncbi.nlm.nih.gov/pubmed/26859304 http://dx.doi.org/10.1371/journal.pone.0148904 |
Sumario: | PURPOSE: Calcification is an important prognostic factor in aortic valve stenosis. However, there is no ultrasound (US) method available to accurately quantify calcification in this setting to date. We aimed to validate a new US method for measuring the amount of calcium in an in vitro model, and compare it to computed tomography (CT), the current imaging gold standard. MATERIALS AND METHODS: An agar phantom (2% agar) was made, containing 9 different amounts of calcium-hydroxyapatite Ca(5)(PO(4))(3)OH (2 to 50mg). The phantoms were imaged with micro-CT and US (10 MHz probe). The calcium area (area(calcium)) and its maximum pixel value (PV(max)) were obtained. These values were summed to calculate CT and US calcium scores (∑(area(calcium) × PV(max))) and volumes (∑area(calcium)). Both US- and CT-calcium scores were compared with the calcium amounts, and with each other. RESULTS: Both calcium scores correlated significantly with the calcium amount (R(2) = 0.9788, p<0.0001 and R(2) = 0.8154, p<0.0001 for CT and US respectively). Furthermore, there was a significant correlation between US and CT for calcium volumes (R(2) = 0.7392, p<0.0001) and scores (R(2) = 0.7391, p<0.0001). CONCLUSION: We developed a new US method that accurately quantifies the amount of calcium in an in vitro model. Moreover it is strongly correlated with CT. |
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