Principal component analysis with pre-normalization improves the signal-to-noise ratio and image quality in positron emission tomography studies of amyloid deposits in Alzheimer's disease

Abstract

This study introduces a new approach for the application of principal component analysis (PCA) with pre-normalization on dynamic positron emission tomography (PET) images. These images are generated using the amyloid imaging agent N-methyl [11C]2-(4′-methylaminophenyl)-6-hydroxy-benzothiazole ([11C]PIB) in patients with Alzheimer's disease (AD) and healthy volunteers (HVs). The aim was to introduce a method which, by using the whole dataset and without assuming a specific kinetic model, could generate images with improved signal-to-noise and detect, extract and illustrate changes in kinetic behavior between different regions in the brain. Eight AD patients and eight HVs from a previously published study with [11C]PIB were used. The approach includes enhancement of brain regions where the kinetics of the radiotracer are different from what is seen in the reference region, pre-normalization for differences in noise levels and removal of negative values. This is followed by slice-wise application of PCA (SW-PCA) on the dynamic PET images. Results obtained using the new approach were compared with results obtained using reference Patlak and summed images. The new approach generated images with good quality in which cortical brain regions in AD patients showed high uptake, compared to cerebellum and white matter. Cortical structures in HVs showed low uptake as expected and in good agreement with data generated using kinetic modeling. The introduced approach generated images with enhanced contrast and improved signal-to-noise ratio (SNR) and discrimination power (DP) compared to summed images and parametric images. This method is expected to be an important clinical tool in the diagnosis and differential diagnosis of dementia.