Improving Stroke Clinical Trials with Medical Imaging and Advanced Algorithms

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dc.contributor.advisorMacDonald, M. Ethan
dc.contributor.advisorGanesh, Aravind
dc.contributor.authorCharatpangoon, Pattarawut
dc.contributor.committeememberBayat, Sayeh
dc.contributor.committeememberMenon, Bijoy
dc.contributor.committeememberMurari, Kartikeya
dc.date2025-06
dc.date.accessioned2024-11-19T19:26:43Z
dc.date.available2024-11-19T19:26:43Z
dc.date.issued2024-11-18
dc.description.abstractClinical trials are essential for advancing healthcare by exploring new treatments to improve patient outcomes. However, recruiting eligible participants for trials remains a significant challenge, particularly in acute conditions like stroke. Stroke is a leading cause of mortality, and timely treatment is critical. While recruiting patients for stroke trials is essential, manual screening for trial eligibility can be slow, prone to errors, and costly. Additionally, the complexity of trial inclusion criteria and the need for rapid decision-making in emergencies make it unrealistic for clinicians to be aware of all ongoing trials. This thesis focuses on three key areas to enhance both the quality and quantity of stroke trial recruitment: 1) the development of automated matching algorithms to improve the number and quality of enrollment; 2) reducing radiation doses in computed tomography perfusion (CTP) imaging to keep patients safer; 3) implementing a multi-site data harmonization pipeline on CTP images to improve the quality of data and ensure consistent and comparable results. The results show that the automated algorithms could quickly and accurately identify eligible participants, reducing human error and bias. The post-processing denoising algorithm effectively reduced the radiation dose by up to 80% while retaining most clinical features. For the harmonization, the variations between sites were mitigated, ensuring more consistent and reliable imaging data. In conclusion, these studies have potential implications that could enhance both the quality and quantity of stroke clinical trials, accelerating medical advancements and promising to improve the outcomes for stroke patients.
dc.identifier.citationCharatpangoon, P. (2024). Improving stroke clinical trials with medical imaging and advanced algorithms (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/120085
dc.language.isoen
dc.publisher.facultySchulich School of Engineering
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectStroke
dc.subjectClinical Trials
dc.subjectMedical Imaging
dc.subjectMatching Algorithms
dc.subjectDenoising Algorithms
dc.subjectData Harmonization
dc.subject.classificationEngineering--Biomedical
dc.subject.classificationNeuroscience
dc.titleImproving Stroke Clinical Trials with Medical Imaging and Advanced Algorithms
dc.typemaster thesis
thesis.degree.disciplineEngineering – Biomedical
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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