Deep-Learning Estimation of Perfusion Kinetic Parameters in Contrast-Enhanced Ultrasound Imaging
Grisan, E., Harput, S., Raffeiner, B., Fiocco, U. and Stramare, R. (2021). Deep-Learning Estimation of Perfusion Kinetic Parameters in Contrast-Enhanced Ultrasound Imaging. IEEE International Symposium on Biomedical Imaging - IEEE ISBI. Nice 13 - 16 Apr 2021 Institute of Electrical and Electronics Engineers (IEEE).
|Authors||Grisan, E., Harput, S., Raffeiner, B., Fiocco, U. and Stramare, R.|
Contrast-enhanced ultrasound (CEUS) is a sensitive imaging technique to evaluate blood perfusion and tissue vascularity, whose quantification can assist in characterizing different perfusion patterns, e.g. in cancer or in arthritis. The perfusion parameters are estimated by fitting non-linear parametric models to experimental data, usually through the optimization of non-linear least squares, maximum likelihood, free energy or other methods that evaluate the adherence of a model adherence to the data. However, low signal-to-noise ratio and the nonlinearity of the model make the parameter estimation difficult.
|Keywords||Parameter estimation; Parametric modelling; Deep learning; Non-linear-least squares; Perfusion estimation; Contrast enhanced ultrasound|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Accepted author manuscript|
File Access Level
|16 Apr 2021|
|Publication process dates|
|Accepted||08 Jan 2021|
|Deposited||02 Feb 2021|
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
3views this month
6downloads this month