Date of Award
5-1-2024
Language
English
Document Type
Master's Thesis
Degree Name
Master of Science (MS)
College/School/Department
Department of Physics
Dissertation/Thesis Chair
Carolyn MacDonald
Committee Members
Ariel Caticha
Subject Categories
Physics
Abstract
A number of medical disorders may only be diagnosed with the use of medical imaging, and X-rays are commonly used to detect fractures, abnormal masses, and cavities. The penetration power of X-rays enables them to effectively see these structures inside the body. However, since background structures like fat or fibrous glandular tissue have similar absorption coefficients, X-ray radiography often face challenges when imaging soft tissues for cancer/tumour diagnosis. Research has shown the theoretical potential for X-ray phase imaging to increase contrast factors by a factor of 100 or more, which theoretically could result in enhanced visualization of tumours. In addition to that, coherent scatter imaging has the potential to detect tumours by recognising the distinct scatter signal of carcinoma compared to normal soft tissue. This work has the eventual aim to develop a system to combine the two techniques. The work in this thesis is primarily to characterize the coherent scatter technique. The experiment utilised a lead shielding slot to create a narrow X-ray beam of rectangular cross section, which was then used for coherent scatter imaging. Different diffraction angles were used to acquire images of fat and graphite, which were used as stand-ins for breast tissue and cancer, respectively. Raw images obtained from the experiment were modified with specialised code to improve the coherent scatter ring visualisation. The results of this study hold significant potential to improve medical imaging techniques for tumour detection, thereby reducing false positive rates and improving the overall accuracy of diagnoses.
Recommended Citation
Boora, Aastha, "The Potential For Enhancing Tumour Detection In Medical Imaging With Coherent Scatter" (2024). Legacy Theses & Dissertations (2009 - 2024). 3293.
https://scholarsarchive.library.albany.edu/legacy-etd/3293
