作者: Alireza Akhbardeh , Hersh Sagreiya , Ahmed El Kaffas , Jürgen K. Willmann , Daniel L. Rubin
DOI: 10.1002/MP.13340
关键词: Artificial intelligence 、 System identification 、 Contrast-enhanced ultrasound 、 Curve fitting 、 Perfusion 、 Frame rate 、 Estimation theory 、 Computer science 、 Ultrasound imaging 、 Region of interest 、 Colorectal cancer 、 Pattern 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 …