Aller au contenu principal
2015 article

Evaluation of normal brain database impact on 3D-SSP images of patients with dementia by using 18F-FDG PET and MRI.

0Citations signalées, ce qui n’est pas une note de qualité
4Institutions déclarées
2Pays d’affiliation déclarés

Rattachement africain : jp, us. Niveau de preuve : code pays fourni par la source.

Le résumé fourni par la source

2622 Objectives The aim of this study was to investigate the effects of properties in normal database (DB) of FDG-PET and MRI brain images, including differences in composition of numbers, ages and genders, on the results of three-dimensional stereotactic surface projection (3D-SSP) images of patients with dementia. Methods In this study, 963 healthy adults (470 female: 53.4±9.9 yrs.; 493 male: 54.0±10.2 yrs.) were included. All subjects were diagnosed as cognitively normal by 3 doctors including a neurologist. Normal DBs for brain FDG-PET and MRI were generated for various combinations of different numbers (10, 20, 40, 50, 100 and 200), age groups (every age, 5 and 10 years) and gender groups (male, female and mixed). Each DB was evaluated for differences in mean, standard deviation (SD) and coefficient of variation (CV) of several brain regions and tested for errors in z-scores of several brain regions on 3D-SSP images of patients with mild cognitive impairment (MCI) and frontotemporal dementia (FTD). Results Normal DVs consisted of more than 50 subjects provided stable mean, SD and CV. In the posterior cingulate cortex (PCC) of a MCI patient, errors of z-scores of DBs composed of 20 subjects distributed up to 2.0 compared with DB composed of 200 subjects. In the anterior cingulate cortex (ACC), age-dependent decline in brain glucose metabolism and atrophy by about 10% were occurred from 40s to 70s. In a patient with FTD, a maximum error of 1.5 in z-score was noted in the ACC if there was a difference of 5 years between a patient age and DB age. Conclusions The 3D-SSP images of patients with dementia changed visually and quantitatively depending on the properties of DB. For the accurate diagnosis, normal DB should be composed of more than 50 subjects matching for age and gender with patients to be analyzed.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.

Le contrôle bibliographique ouvert

Aucun DOI disponible pour le contrôle Crossref.

Les institutions déclarées

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

Radiomics and Machine Learning in Medical ImagingMedical Imaging Techniques and Applications

BNTIC News n’est pas le producteur de ces données. Les publications sont interrogées à la demande dans Crossref, OpenAIRE, DOAJ, Europe PMC, HAL, DataCite, AfricArXiv, ROR et la Banque mondiale, sans clé d’accès. OpenAlex reste optionnel. Aucun service payant n’est nécessaire et aucune donnée externe n’est enregistrée en base. Consulter les sources et leurs limites.