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. We also compared the histopathology-derived SUVR threshold with one derived from healthy controls. METHODS: Data from 345 consenting subjects enrolled in 8 prior clinical trials of (18)F-flutemetamol injection were used. We grouped subjects into 3 cohorts: an autopsy cohort (n = 68) comprising terminally ill patients with postmortem confirmation of brain amyloid status; a test cohort (n = 172) comprising 33 patients with clinically probable Alzheimer disease, 80 patients with mild cognitive impairment, and 59 healthy volunteers; and a healthy cohort of 105 volunteers, used to define a reference range for SUVR. Visual image categorizations for comparison were from a previous study. A fully automated PET-only quantification method was used to compute regional neocortical SUVRs that were combined into a single composite SUVR. An SUVR threshold for classifying scans as positive or negative was derived by ranking the PET scans from the autopsy cohort based on their composite SUVR and comparing data with the standard of truth based on postmortem brain amyloid status for subjects in the autopsy cohort. The derived threshold was used to categorize the 172 scans in the test cohort as negative or positive, and results were compared with categorization using visual assessment. Different reference and composite region definitions were assessed. Threshold levels were also compared with corresponding thresholds derived from the healthy group. RESULTS: Automated quantification (using pons as the reference region) demonstrated 91% sensitivity and 88% specificity and gave 3 false-positive and 4 false-negative scans. All 3 false-positive cases were either borderline-normal by standard of truth or had moderate to heavy cortical diffuse plaque burden. In the test cohort, the concordance between quantitative and visual read categorization ranged from 97.1% to 99.4% depending on the selection of reference and composite regions. The threshold derived from the healthy group was close to the histopathology-derived threshold. CONCLUSION: Categorization of (18)F-flutemetamol amyloid imaging data using an automated PET-only quantification method showed good agreement with histopathologic classification of neuritic plaque density and a strong concordance with visual read results.

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 (Aß-) and positive (Aß+) 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. Linear regression of voxel intensities on the standardized uptake value ratio (SUVR) in a neocortical composite region for all scans gave an intercept image and a slope image. We devised a method where an adaptive template image spanning the uptake range (the most Aß- to the most Aß+ image) can be generated through a linear combination of these 2 images and where the optimal template is selected as part of the registration process. We applied the method to the (18)F-flutemetamol phase II data using a fixed volume of interest atlas to compute SUVRs. Validation was performed in several steps. The PET-only adaptive template registration method and the MR imaging-based method used in statistical parametric mapping were applied to spatially normalize PET and MR scans, respectively. Resulting transformations were applied to coregistered gray matter probability maps, and the quality of the registrations was assessed visually and quantitatively. For comparison of quantification results with an independent patient-space method, FreeSurfer was used to segment each subject's MR scan and the parcellations were applied to the coregistered PET scans. We then correlated SUVRs for a composite neocortical region obtained with both methods. Furthermore, to investigate whether the (18)F-flutemetamol model could be generalized to (11)C-Pittsburgh compound B ((11)C-PIB), we applied the method to Australian Imaging, Biomarkers and Lifestyle (AIBL) (11)C-PIB scans (n = 285) and compared the PET-only neocortical composite score with the corresponding score obtained with a semimanual method that made use of the subject's MR images for the positioning of regions. RESULTS: Spatial normalization was successful on all scans. Visual and quantitative comparison of the new PET-only method with the MR imaging-based method of statistical parametric mapping indicated that performance was similar in the cortical regions although the new PET-only method showed better registration in the cerebellum and pons reference region area. For the (18)F-flutemetamol quantification, there was a strong correlation between the PET-only and FreeSurfer SUVRs (Pearson r = 0.96). We obtained a similar correlation for the AIBL (11)C-PIB data (Pearson r = 0.94). CONCLUSION: The derived adaptive template registration method allows for robust, accurate, and fully automated quantification of uptake for (18)F-flutemetamol and (11)C-PIB scans without the use of MR imaging data.

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: Aß 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). Clinical follow-up was performed 2 years after the initial assessment. RESULTS: Hippocampus volumes showed extensive overlap between AD and HV cases while PET SUVRs showed clear group clustering. When both measures were combined, there was a relatively compact cluster of HV scans and a less compact AD cluster. MCI cases had a bimodal distribution of SUVRs. [(18)F]Flutemetamol-positive MCI subjects showed a large variability in hippocampus volumes, indicating that these subjects were in different stages of neurodegeneration. Some [(18)F]flutemetamol-negative MCI scans had hippocampus volumes that were well below the HV range. Clinical follow-up showed that 8 of 9 MCI to AD converters came from the [(18)F]flutemetamol-positive group. CONCLUSION: Combining [(18)F]flutemetamol PET with structural MRI provides additional information for categorizing disease and potentially predicting shorter time to progression from MCI to AD, but this has to be validated in larger longitudinal studies.

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. The method produced a high similarity index, 0.87, and correlation coefficient, 0.94, with semi-automatically generated segmentations. When comparing hippocampus volumes extracted from 1.5T and 3T images, the absolute value of the difference was low: 3.2% of the volume. The correct classification rate for Alzheimer's disease and cognitively normal cases was about 80% while the accuracy 65% was obtained for classifying stable and progressive mild cognitive impairment cases. The method was evaluated in three cohorts consisting altogether about 1000 cases, the main emphasis being in the analysis of the ADNI cohort. The computation time of the method is about 2 minutes on a standard laptop computer. The results show a clear potential for applying the method in clinical practice.

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. Evaluation of the method was performed using SPECT scans of 79 subjects including 42 patients with clinical diagnosis of AD and 37 controls. It was found that combination of intensity and gradient features in co-occurrence matrices captures significant differences in asymmetry values between AD and normal controls (p < 0.00003 for all cerebral lobes). Our results suggest that the asymmetry feature is useful for discriminating AD patients from normal controls as detected by SPECT.

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. Forty-six patients with AD, 7 patients with FLD and 34 normal controls (CTR) were included in the study. rCBF was assessed by technetium-99m hexamethylpropylene amine oxime and a three-headed single-photon emission tomography (SPET) camera. A brain atlas was used to define volumes of interest (VOIs) corresponding to the brain lobes. In addition 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 belonged 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 and parietal lobes, accuracy 98%). For AD and FLD the count density/voxel histogram in the frontal, parietal and occipital lobes classified the groups with the highest 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 correctly recognised 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 analytical methods used for rCBF-SPET. This was achieved by extracting from the SPET data the intrinsic information content in each of the selected VOIs.