Peripheral perfusion imaging modeling

Effective clutter and noise filtering is critical for contrast-free ultrasonic per- fusion imaging. Novel pulsed-Doppler methods for perfusion imaging are val- idated using dialysis cartridges as perfusion phantoms. Techniques are exam- ined quantitatively at 5 and 12.5 MHz using phantoms with blood-mimicking fluid flow within cellulose microfibers. A variety of flow states are assessed to measurement sensitivity and flow accuracy. Comparisons between phantom and preclinical images show that peripheral perfusion imaging can be reliably achieved without contrast enhancement. One goal is to examine methods for setting the parameters of higher-order singular value decomposition (HOSVD) clutter filter and characterize blood and clutter components. 1-D and 2-D echo simulators are used to model in various flow states and clutter motion with exactly known signals. We explore the strengths and limitations of principal component analysis (PCA) and independent component analysis (ICA) for clutter and noise filtering. PCA methods excel when the echo covariance exhibits a significant blood- scattering component orthogonal to the tissue clutter component. Another goal is to improve the reliability and adaptivity of the HOSVD filter. We evaluate the performance of normalized cross-correlation (NCC) displacement estimation algorithm and Fourier-based registration methods. The remarkable advantage of image registration methods and their ability to help reduce the bandwidth of coherent clutter motion improves the performance of clutter filter on peripheral perfusion imaging.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Bioengineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Zhu, Yang

Contributors dc:contributor

• Insana, Michael F
• Sutton, Brad
• Dobrucki, Wawrzyniec
• Oelze, Michael L

• Ultrasound power Doppler
• scatterer statistics
• clutter filtering
• basis decomposition
• independent and principal component analyses

Statement dc:rights

• Copyright 2021 Yang Zhu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/113124

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/113124