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Primerjava statistične parametrične kartografije in skaliranega subprofilnega modela pri pacientih z Alzheimerjevo boleznijo
ID Hočuršćak, Lara (Author), ID Simončič, Urban (Mentor) More about this mentor... This link opens in a new window

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Abstract
Alzheimerjeva bolezen je najpogostejši vzrok demence pri starejši populaciji, ki predstavlja več kot 50 % vseh demenc. Pojavnost te bolezni se viša zaradi starajoče populacije, zato je pomembna zgodnja diagnostika. Slikanje porazdelitve $^{18}$F-fluorodeoksiglukoze s pozitronsko emisijsko tomografijo (PET) meri presnovno aktivnost, kar omogoča prepoznavanje možganskih regij z zmanjšano presnovo, ki so značilne za posamezne nevrodegenerativne bolezni. V zgodnjih fazah Alzheimerjeve demence in pri pacientih z blago kognitivno motnjo so odstopanja v presnovi bolnikov v primerjavi z zdravimi preiskovanci lahko relativno majhna, pri čemer nam vizualni pregled PET slik ne da vseh informacij. Takrat se poslužujemo statističnih metod, kot sta na primer statistična parametrična kartografija (SPM) in skaliran subprofilni model na osnovi analize glavnih komponent (SSM/PCA). Kljub temu da sta metodi v osnovi različni in sta njuna rezultata različni količini, je pri vizualni primerjavi rezultatov obeh metod mogoče opaziti podobnosti. V nalogi smo analitično izpeljali povezavo med SPM in SSM/PCA metodama. Teoretično napovedano podobnost med njima smo preverili tako na kliničnih slikah možganov kot na simuliranih slikah, s čimer smo razjasnili povezavo med rezultatoma SPM in SSM/PCA analize. Ugotovili smo, da je rezultat SSM/PCA metode sorazmeren logaritmu za 1 povečanega produkta rezultata SPM metode in korekcijskega faktorja, ki je povezan z relativno negotovostjo slike.

Language:Slovenian
Keywords:Alzheimerjeva demenca (AD), blaga kognitivna motnja (BKM), FDG-PET, SPM, SSM/PCA, ADRP, MCIRP
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FMF - Faculty of Mathematics and Physics
Year:2020
PID:20.500.12556/RUL-121269 This link opens in a new window
COBISS.SI-ID:31628547 This link opens in a new window
Publication date in RUL:02.10.2020
Views:780
Downloads:95
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Secondary language

Language:English
Title:Comparison of statistical parametric mapping and scaled subprofile model in patients with Alzheimer's disease
Abstract:
Alzheimer's disease is the most prevalent type of dementia amongst older population and it represents more than 50 % of all types of dementia. Incidence of this disease increases with aging population, stressing the importance of early diagnostics. Positron emission tomography (PET) imaging of $^{18}$F-fluorodeoxyglucose distribution measures metabolic activity, enabling us to recognise brain regions with lowered metabolic activity that is characteristic for the individual neurodegenerative disease. In an early phase of Alzheimer's dementia or in patients with mild cognitive impairment deviation from normal metabolic activity can be relatively small, meaning we can not get sufficient information from visually inspecting PET images. This is when statistical methods such as statistical parametric mapping (SPM) and scaled subprofile model/principal component analysis (SSM/PCA) are used. Despite the fundamental differences between those two methods and their results, visual comparison of the results implies resemblance between methods. In the thesis, comparison between SPM and SSM/PCA methods was analytically derived. Theoretically predicted resemblance between those methods has been verified by analysing clinical images as well as simulated noise images, which was used to clarify the connection between SPM and SSM/PCA results. We have discovered that SSM/PCA result is proportional to the logarithm of sum of number 1 and the product of SPM result and correction factor, related to relative image uncertainty.

Keywords:Alzheimer's dementia (AD), mild cognitive impairment (MCI), FDG-PET, SPM, SSM/PCA, ADRP, MCIRP

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