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University of Ontario Institute of Technology

Electrical impedance tomography for internal radiation therapy

Abstract

dc:description.abstract

Internal radiation therapy is a radiation-based treatment for various forms of localized cancer. In this treatment, several needles or catheters are inserted percutaneously into the tissue, and radiation is delivered through them directly to the site of the tumour growth. Imaging methods to delineate the dominant tumour are imperative to ensure the maximum success of the radiation procedure. This thesis investigates a new imaging approach for internal radiation therapy based on the principle of electrical impedance tomography (EIT). A novel procedure utilizing brachytherapy needles as electrodes is proposed to map the internal electrical conductivity of the tissue. Since cancerous tissue exhibits different levels of conductivity than healthy tissue, it is hypothesized that the electrical conductivity map of the tissue can be used to delineate cancerous nodules via EIT. In addition, this thesis explores the use of electrical impedance modulation via ultrasound to improve the spatial resolution of EIT images.

Degree

thesis:*
Name thesis:degree_name
Master of Applied Science (MASc)
Discipline thesis:degree_discipline
Mechanical Engineering
Grantor
University of Ontario Institute of Technology
Year dc:date.issued
2021

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Tan, Hao Chen
Advisors dc:contributor.advisor
  • Rossa, Carlos
  • Seo, Jaho

Subjects

dc:subject × 5

Rights

Language dc:language.iso
en

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/10155/1357
OAI identifier oai:identifier
oai:ontariotechu.scholaris.ca:10155/1357

Chain of custody

source
Harvested from
Ontario Institute of Technology
Base URL
ontariotechu.scholaris.ca/server/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Tan, Hao Chen. Electrical impedance tomography for internal radiation therapy. University of Ontario Institute of Technology, 2021. https://hdl.handle.net/10155/1357