A multi-model framework to estimate perfusion parameters using contrast-enhanced ultrasound imaging.

作者: Alireza Akhbardeh , Hersh Sagreiya , Ahmed El Kaffas , Jürgen K. Willmann , Daniel L. Rubin

DOI: 10.1002/MP.13340

关键词: Artificial intelligenceSystem identificationContrast-enhanced ultrasoundCurve fittingPerfusionFrame rateEstimation theoryComputer scienceUltrasound imagingRegion of interestColorectal cancerPattern recognition

摘要: Purpose Contrast‐enhanced ultrasound imaging has expanded the diagnostic potential of ultrasound by enabling real‐time imaging and quantification of tissue perfusion. Several perfusion models and curve fitting methods have been developed to quantify the temporal behavior of tracer signal and standardize perfusion quantification. While the least‐squares approach has traditionally been applied for curve fitting, it can be inadequate for noisy and complex data. Moreover, previous research suggests that certain perfusion models may be …

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