Ultrasound tissue perfusion imaging

Enhanced blood perfusion in a tissue mass is an indication of neo-vascularity and potential malignancy. Ultrasonic pulsed Doppler imaging is a safe and economical modality for noninvasive monitoring of blood flow. However, weak blood echoes make it difficult to detect perfusion using standard methods without the expense of contrast enhancement. Additionally, imaging requires high sensitivity to slow, disorganized blood-flow patterns while simultaneously rejecting clutter and noise. An approach to address these challenges involves arranging acquisition data in a multi-dimensional structure to facilitate the characterization and separation of independent scattering sources. The resulting data array involves a linear combination of spatial, slow-time (kHz-order sampling), and frame-time (Hz-order sampling) coordinates. Applying an eigenfilter that exploits higher-order singular value decomposition (HOSVD) can technically transform the array and reduce the dimensions to yield power estimates for blood flow and perfusion that are well isolated from tissue clutter. Studies using microcirculation-mimicking simulations and phantoms enable the optimization of the filtering algorithm to maximize estimation efficiency. These techniques are applied to murine models of ischemia and melanoma at 24 MHz to form perfusion images. The results show enhancements of tissue perfusion maps, which help researchers access lesions without contrast enhancement. In a study aimed at peripheral artery disease (PAD), the enhanced sensitivity and specificity of ultrasonic-pulsed-Doppler imaging enable differentiation of perfusion between healthy and ischemic states. In addition, the use of the new ultrasound imaging coupled with other imaging modalities helps to illuminate the complex mechanism that mediates neovascularization in response to vascular occlusion. Consequently, these techniques have the potential to increase the effectiveness of existing medical imaging technologies in safe, cost-effective ways that promote sustainable medicine.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2018

Author dc:creator

• Kim, Minwoo

Contributors dc:contributor

• Insana, Michael F.
• Liang, Zhi-Pei
• Oelze, Michael L.
• Dobrucki, Wawrzyniec L.

• Ultrasound
• Perfusion Imaging
• Vascular Imaging
• Power Doppler
• Adaptive Eigenfilter
• Higher-order Singular Value Decomposition

Statement dc:rights

• Copyright 2018 Minwoo Kim

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/101453

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/101453