A novel quantitative approach to positron emission tomography for the diagnosis of Alzheimer’s disease
| dc.contributor.author | Katako, Audrey | |
| dc.contributor.examiningcommittee | Vrontakis, Maria (Human Anatomy and Cell Science) Goertzen, Andrew (Radiology) | en_US |
| dc.contributor.supervisor | Ko, Ji Hyun (Human Anatomy and Cell Science) | en_US |
| dc.date.accessioned | 2017-08-17T18:51:51Z | |
| dc.date.available | 2017-08-17T18:51:51Z | |
| dc.date.issued | 2017 | |
| dc.degree.discipline | Human Anatomy and Cell Science | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | The incidence of Alzheimer’s disease (AD) amongst the elderly in Canada (age >65) is expected to grow with increasing life expectancy. Current diagnostic methods are qualitative and yield equivocal results whose unreliability is exacerbated by variations in physician experience and technique. Therefore, there is a need for a quantitative method for interpreting Positron Emission Tomography (PET) brain scans. The method should be sensitive, specific, and capable of distinguishing between affected and unaffected individuals even in early disease stages. Here, scaled subprofile modeling/principal component analysis (SSM/PCA) and machine voting were used with 763 subjects from the Alzheimer’s disease Neuroimaging Initiative database and 99 subjects referred to the Health Sciences Centre – Winnipeg PET center between 2010 and 2012 to generate a machine voting score for Alzheimer’s disease (MVAD), which can distinguish between progressors and non-progressors from mild cognitive impairment to AD. | en_US |
| dc.description.note | October 2017 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/32355 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Alzheimer's disease, FDG-PET,SSMPCA,Machine voting | en_US |
| dc.title | A novel quantitative approach to positron emission tomography for the diagnosis of Alzheimer’s disease | en_US |
| dc.type | master thesis | en_US |