Tuberculosis is a major concern in low- and middle-income countries, but may be diagnosed using standard chest x-rays. Conventional film-screen x-ray detectors require more maintenance than digital x-ray detectors and need a good supply chain of developer chemicals and film, which make them difficult to maintain in low-infrastructure areas of the world. Current digital x-ray technology is prohibitively expensive for this market, although it brings the possibility of tele-radiology and tele-medicine, quicker diagnosis time, and virtually no cost per test compared to other diagnostics for tuberculosis. This thesis examines the requirements on a small-sized, low-cost digital x-ray detector for this application.
Two small x-ray detectors were integrated into x-ray systems, then characterized for detector performance using metrics known as modulation transfer function, noise power spectrum, and detective quantum efficiency. The system designs and the results of the experiments are shown. Details are also shown about the setup of the x-ray lab, including the door interlock system for a lead-lined x-ray cabinet.
To determine whether a smaller x-ray detector is diagnostically accurate enough for tuberculosis diagnosis compared to full-size chest radiography equipment, a medical study was designed and run using a web-based survey of radiologists in Pakistan, where tuberculosis is a recognized disease.
In an attempt to compare x-ray detector performance, MATLAB code was written to measure the modulation transfer function, noise power spectrum, and detective quantum efficiency of x-ray systems. The details about this code, and challenges in simulating the performance of physically different detectors are explained in the thesis.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OWTU.10012/8249 |
| Date | January 2014 |
| Creators | Mann, Ryan Scott |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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