Using CT Data to Improve the Quantitative Analysis of 18F-FBB PET Neuroimages

Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network

Alzheimer's Disease (AD) is a progressive, neurodegenerative brain disease and is an incurable ailment. No drug exists for AD, but its progression can be delayed if the disorder is identified at its initial stage. Therefore, an early analysis of AD is of fundamental importance for patient care and efficient treatment. Neuroimaging techniques aim to assist the physician in the diagnosis of brain disorders by using images. Positron emission tomography (PET) is a kind of neuroimaging technique employed to create 3D images of the brain. Due to many PET images, researchers attempted to develop computer-aided diagnosis (CAD) to differentiate normal control from AD. Most of the earlier methods used image processing techniques for preprocessing and attributes extraction and then developed a model or classifier to classify the brain images. As a result, the retrieved features had a significant impact on the recognition rate of previous techniques. A novel and enhanced CAD system based on a convolutional neural network (CNN) is formulated to address this issue, capable of discriminating normal control from Alzheimer's disease patients. The proposed approach is evaluated using the 18FDG-PET images of 855 patients, including 635 normal control and 220 Alzheimer's disease patients from the ADNI database. The result showed that the proposed CAD system yields an accuracy of 96%, a sensitivity of 96%, and a specificity of 94%, leading to splendid performance when related to the methods already in use that are specified in the literature.

Different patterns of β-amyloid deposition in patients with Alzheimer's disease according to the presence of mild parkinsonism

This study aimed to compare the patterns of β-amyloid deposition between patients with early-stage Alzheimer's disease (AD) with mild parkinsonism and those without parkinsonism. Sixty-one patients with early-stage AD (Clinical Dementia Rating [CDR], 0.5 or 1) who underwent ¹⁸F-florbetaben (¹⁸F-FBB) PET scans were enrolled. We performed comparative analyses of regional FBB uptake in the frontal, parietal, lateral temporal, medial temporal, occipital, anterior cingulate, and posterior cingulate cortices and in the precuneus, striatum, and thalamus between AD patients with mild parkinsonism (AD-p+; n = 23) and those without parkinsonism (AD-p-; n = 38). There was no significant difference in age, sex, years of education, Mini-Mental State Examination score, and white matter hyperintensity severity between groups. The AD-p+ group had lower composite scores in frontal/executive function domain than the AD-p- group. The AD-p+ group had a higher FBB uptake in the occipital cortex, but not in other cortical regions, than the AD-p- group. Our findings suggest that additional β-amyloid deposition in the occipital region is associated with mild parkinsonism in early-stage AD.