Modern Applications of Quantitative Ultrasound for Medical Research

Presentation Abstract:

Quantitative ultrasound (QUS) is an active research field focused on obtaining quantitative tissue properties (i.e., system- and user-independent) from ultrasound data. Conventional ultrasound imaging is commonly used to visualize soft tissue morphology. During scanning, a gray-scale B-mode image is displayed on screen from which a trained clinician can evaluate tissue states. However, B-mode ultrasound image formation discards valuable information in the raw backscattered echo signal that encodes information about tissue microstructure. Therefore, microstructural changes in soft tissues that accompany disease processes, but do not directly affect tissue morphology, may not be visible in B-mode images. QUS methods use the raw ultrasound data to reconstruct parametric maps that are representative of tissue microstructure. In this talk, I will review conventional ultrasound imaging and QUS methods based on analyzing the backscatter coefficient and envelope statistics. I will present recent vivo QUS results from in vivo human studies in cancer, ophthalmology, and dermatology.

Dr. Jonathan Mamou graduated in 2000 from the Ecole Nationale Supérieure des Télécommunications in Paris, France. In January 2001, he began his graduate studies in Electrical and Computer Engineering at the University of Illinois in Urbana-Champaign, Urbana, IL. He received his M.S. and Ph.D. degrees in May 2002 and 2005, respectively. He previously was the Associate Research Director of the F. L. Lizzi Center for Biomedical Engineering at Riverside Research in New York, NY. He currently is a Professor of Electrical Engineering in the Department of Radiology of Weill Cornell Medicine in New York, NY. Dr. Mamou also is an Adjunct Professor in the Department of Electrical Engineering of New York University. His fields of interest include theoretical aspects of ultrasound scattering, ultrasonic medical imaging, acoustic microscopy, ultrasound contrast agents, and biomedical image processing.