@inproceedings{e6931da13767404bb30fef15a724d4e8,
title = "Informational modeling of tissue-like materials using ultrasound",
abstract = "The correlation between disease pathology and tissue stiffness can be exploited to detect and potentially diagnose abnormal tissue states. Elastography is an imaging modality that attempts to image tissue stiffness by measuring local displacements caused by an applied force and calculating a strain map. Some elasticity imaging techniques attempt to assign a material parameter, such as Young's or shear modulus, to the imaged region in an effort to increase specificity. Unfortunately, the inversion techniques require many simplifying assumptions which lead to errors in the parameter estimates. One possible solution to increase accuracy in estimation is to first build an empirical model of the tissue using measured force-displacement data, thus eliminating the need for a priori assumptions. We propose the use of informational models for this purpose.",
keywords = "Elasticity Imaging, Machine Learning, Neural Networks",
author = "Cameron Hoerig and Jamshid Ghaboussi and Michael Insana",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 ; Conference date: 16-04-2015 Through 19-04-2015",
year = "2015",
month = jul,
day = "21",
doi = "10.1109/ISBI.2015.7163858",
language = "English (US)",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "239--242",
booktitle = "2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015",
}