Classification of amyloid status using machine learning with histograms of oriented 3D gradients

AbstractBrain amyloid burden may be quantitatively assessed from positron emission tomography imaging using standardised uptake value ratios. Using these ratios as an adjunct to visual image assessment has been shown to improve inter-reader reliability, however, the amyloid positivity threshold is dependent on the tracer and specific image regions used to calculate the uptake ratio. To address this problem, we propose a machine learning approach to amyloid status classification, which is independent of tracer and does not require a specific set of regions of interest. Our method extracts feature vectors from amyloid images, which are based on histograms of oriented three-dimensional gradients. We optimised our method on 133 18F-florbetapir brain volumes, and applied it to a separate test set of 131 volumes. Using the same parameter settings, we then applied our method to 209 11C-PiB images and 128 18F-florbetaben images. We compared our method to classification results achieved using two other methods: standardised uptake value ratios and a machine learning method based on voxel intensities. Our method resulted in the largest mean distances between the subjects and the classification boundary, suggesting that it is less likely to make low-confidence classification decisions. Moreover, our method obtained the highest classification accuracy for all three tracers, and consistently achieved above 96% accuracy

Analysis of asymmetries in ictal and inter-ictal SPECT images for the localization of epileptic foci

In patients suffering from focal epilepsy, the region of seizure onset is characterized by hyperperfusion during the seizure (ictal state), and hypoperfusion in normal (inter-ictal) state. For diagnosis and surgery planning in epilepsy patients, ictal and inter-ictal SPECT images play a major role. However, comparison of this kind of data is a difficult clinical problem due to varying physiological uptake in brain regions. For this reason, different analysis methods such as standard reading by a clinical expert, comparison to a normal database, subtraction ictal SPECT co-registered to MRI (SISCOM) or asymmetry analysis are applied, for a comprehensive analysis of the data. The latter is usually hampered by functional and anatomical asymmetries which aggravate analysis. For this reason, a novel approach for asymmetry analysis is presented in this work, which overcomes the aforementioned limitations. The approach was successfully applied to SPECT datasets of 10 epilepsy patients

Multiple Discriminant Analysis of SPECT Data for Alzheimer’s Disease, Frontotemporal Dementia and Asymptomatic Controls

Multiple discriminant analysis (MDA) is a generalization of the Fisher discriminant analysis (FDA) and makes it possible to discriminate more than two classes by projecting the data onto a subspace. In this work, it was applied to technetium- 99methylcysteinatedimer (99mTc-ECD) SPECT datasets of 10 Alzheimer’s disease (AD) patients, 11 frontotemporal dementia (FTD) patients and 11 asymptomatic controls (CTR). Principal component analysis (PCA) was used for dimensionality reduction, followed by projection of the data onto a discrimination plane via MDA. In order to separate the different groups, linear boundaries were calculated by applying FDA to two classes at a time (linear machine). By executing the F-test for different numbers of principal components and examining the corresponding classification accuracy, an optimal discrimination plane based on the first three principal components was determined. In order to further assess the method, another dataset comprising patients with early-onset AD and FTD (beginning or suspected disease) was projected by the same method onto this discrimination plane, resulting in a correct classification for most cases. The successful iscrimination of another dataset on the same plane indicates that the model is well suited to account fordisease-specific characteristics within the classes, even for patients with early-onset AD and FTD

Hemihyposmia in a case of hemiparkinsonism

Olfactory dysfunction in Parkinson's disease is becoming more relevant. We report on a 38year old woman with right-sided hemiparkinsonism classified 1 degree of severity according to the Hoehn and Yahr scale. Despite not complaining of smell disturbances olfactory testing revealed pronounced hemihyposmia on the ipsilateral side of the marked decreased tracer uptake in the 123I-FP-CIT SPECT. To our knowledge pronounced hemihyposmia has been rarely described in patients with early signs and symptoms of Parkinson's disease

Classification accuracy of multivariate analysis applied to 99mTc-ECD SPECT data in Alzheimer s disease patients and asymptomatic controls

With increasing life expectancy in developed countries, there is a corresponding increase in the frequency of diseases typically associated with old age, in particular dementia. In recent research, multivariate analysis of Positron Emission Tomography (PET) datasets has shown potential for classification between Alzheimer s disease (AD) patients and asymptomatic controls. In this work, the feasibility of multivariate analysis using Principal Component Analysis (PCA) and Fisher Discriminant Analysis (FDA) of Single Photon Emission Computed Tomography (SPECT) data is investigated. In order to obtain robust and reliable results, bootstrap resampling is applied and the robustness and classification accuracy of PCA/FDA are investigated. The robustness of the analysis is assessed by estimating the distribution of the angle between PCA/FDA discriminative vectors generated by bootstrap resampling, and the classification predictive accuracy is assessed using the .632 bootstrap estimator. The results indicate that PCA/FDA on SPECT data enables a robust differentiation between AD patients and asymptomatic controls based on three principal components, with a classification accuracy of 89%