A new approach for improving diagnostic accuracy of Alzheimer Disease and Frontal Lobe Dementia utilizing the intrinsic properties of the SPET data set.

Alzheimer’s Disease (AD) and Frontal Lobe Dementia (FLD) show characteristic patterns of regional cerebral blood flow (rCBF). However when compared to elderly normal individuals such patterns might superimpose to that of aging brain. The aim of this study is to propose a new method for better classification and recognition of AD and FLD cases when compared to normal controls. Forty-six AD and 7 FLD patients and 34 normal controls (CTR) were included in the study. rCBF was assessed by 99mTc-HMPAO and a three-headed SPET camera. A brain atlas was used to define volumes of interest (VOIs) corresponding to the brain lobes. Counterbalanced to conventional image processing methods, based on count-density/voxel, the new approach also analysed other intrinsic properties of the data by means of gradient computation steps. Hereby, five factors were assessed and tested separately – the mean count-density/voxel and its histogram, the mean gradient and its histogram and the gradient angle co-occurrence matrix. A feature vector concatenating single features was also created and tested. Preliminary feature discrimination was performed using a two-sided t-test and a K-means clustering was then used to classify the image sets into categories. Finally, five-dimensional co-occurrence matrices combining the different intrinsic properties were computed for each VOI and their ability to recognise the group to which each individual scan belongs to was investigated. For correct classification of the AD-CTR groups, the gradient histogram in the parieto-temporal lobes was the most useful single feature (accuracy 91%). FLD and CTR were better classified by the count-density/voxel histogram (frontal and occipital lobes) and by the mean gradient (frontal, temporal, parietal lobes, accuracy 98%). As for AD and FLD the count-density/voxel histogram in the frontal, parietal and occipital lobes classified the groups with the higher accuracy (85%). The concatenated joint feature correctly classified 96% of the AD-CTR, 98% of the FLD-CTR and 94% of the AD-FLD cases. 5D co-occurrence matrices recognised correctly 98% of the AD-CTR cases, 100% of the FLD-CTR cases and 98% of the AD-FLD cases. The proposed approach classified and diagnosed AD and FLD patients with higher accuracy than conventional analysing methods used for rCBF-SPET. This was achieved by extracting from the SPET data the intrinsic information content in each of the selected volumes of interest.

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Pagani M
Kovalev VA
Lundqvist R
Jacobsson H
Larsson SA
Springer., Heidelberg;, Germania
European journal of nuclear medicine and molecular imaging (Print) 30 (2003): 1481–1488. doi:10.1007/s00259-003-1196-z
info:cnr-pdr/source/autori:Pagani M, Kovalev VA, Lundqvist R, Jacobsson H, Larsson SA and Thurfjell/titolo:A new approach for improving diagnostic accuracy of Alzheimer Disease and Frontal Lobe Dementia utilizing the intrinsic properties of the SPET data
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Ritratto di Marco Pagani
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