Automated quantification of 18F-flutemetamol PET activity for categorizing scans as negative or positive for brain amyloid: concordance with visual image reads.

Unlabelled: Clinical trials of the PET amyloid imaging agent (18)F-flutemetamol have used visual assessment to classify PET scans as negative or positive for brain amyloid. However, quantification provides additional information about regional and global tracer uptake and may have utility for image assessment over time and across different centers. Using postmortem brain neuritic plaque density data as a truth standard to derive a standardized uptake value ratio (SUVR) threshold, we assessed a fully automated quantification method comparing visual and quantitative scan categorizations.

Implementation and validation of an adaptive template registration method for 18F-flutemetamol imaging data.

Unlabelled: The spatial normalization of PET amyloid imaging data is challenging because different white and gray matter patterns of negative (Aβ-) and positive (Aβ+) uptake could lead to systematic bias if a standard method is used. In this study, we propose the use of an adaptive template registration method to overcome this problem.

Methods: Data from a phase II study (n = 72) were used to model amyloid deposition with the investigational PET imaging agent (18)F-flutemetamol.

Combination of biomarkers: PET [18F]flutemetamol imaging and structural MRI in dementia and mild cognitive impairment.

Background: The New National Institute on Aging-Alzheimer's Association diagnostic guidelines for Alzheimer's disease (AD) incorporate biomarkers in the diagnostic criteria and suggest division of biomarkers into two categories: Aβ accumulation and neuronal degeneration or injury.

Objective: It was the aim of this study to compute hippocampus volume from MRI and a neocortical standard uptake value ratio (SUVR) from [(18)F]flutemetamol PET and investigate the performance of these biomarkers when used individually and when combined.

Methods: Fully automated methods for hippocampus segmentation and for computation of neocortical SUVR were applied to MR and scans with the investigational imaging agent [(18)F]flutemetamol in a cohort comprising 27 AD patients, 25 healthy volunteers (HVs) and 20 subjects with amnestic mild cognitive impairment (MCI).

Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer's disease.

Assessment of temporal lobe atrophy from magnetic resonance images is a part of clinical guidelines for the diagnosis of prodromal Alzheimer's disease. As hippocampus is known to be among the first areas affected by the disease, fast and robust definition of hippocampus volume would be of great importance in the clinical decision making. We propose a method for computing automatically the volume of hippocampus using a modified multi-atlas segmentation framework, including an improved initialization of the framework and the correction of partial volume effect.

Asymmetry of SPECT perfusion image patterns as a diagnostic feature for Alzheimer's disease.

In this paper we propose a new diagnostic feature for Alzheimer's Disease (AD) which is based on assessment of the degree of inter-hemispheric asymmetry using Single Photon Emission Computed Tomography (SPECT). The asymmetry measure used represents differences in 3D perfusion image patterns in the cerebral hemispheres. We start from the simplest descriptors of brain perfusion such as the mean intensity within pairs of brain lobes, gradually increasing the resolution up to five-dimensional co-occurrence matrices.

A new approach for improving diagnostic accuracy in Alzheimer's disease and frontal lobe dementia utilising the intrinsic properties of the SPET dataset.

Alzheimer's disease (AD) and frontal lobe dementia (FLD) show characteristic patterns of regional cerebral blood flow (rCBF). However, these patterns may overlap with those observed in the aging brain in elderly normal individuals. The aim of this study was to develop a new method for better classification and recognition of AD and FLD cases as compared with normal controls.