# Texture-Based Analysis of \({}^{\bf 18}\)F-Labeled Amyloid PET Brain Images
###### Abstract  
Amyloid positron emission tomography (PET) brain imaging with radiotracers like \([^{18}\)F]forbtebapir (FBP) or \([^{18}\)F]fluttemamol (FMM) is frequently used for the diagnosis of [PERSON]'s disease. Quantitative analysis is usually performed with standardized uptake value ratios (SUVR), which are calculated by normalizing to a reference region. However, the reference region could present high variability in longitudinal studies. Texture features based on the grey-level co-occurrence matrix, also called Haralick features (HF), are evaluated in this study to discriminate between amyloid-positive and negative cases. A retrospective study cohort of 66 patients with amyloid PET images (30 \([^{18}\)F]FBP and 36 \([^{18}\)F]FMM) was selected and SUVRs and 6 HFs were extracted from 13 cortical volumes of interest. Mann-Whitney U-tests were performed to analyze differences of the features between amyloid positive and negative cases. Receiver operating characteristic (ROC) curves were computed and their area under the curve (AUC) was calculated to study the discriminatory capability of the features. SUVR proved to be the most significant feature among all tests with AUCs between 0.692 and 0.989. All HFs except correlation also showed good performance. AUCs of up to 0.949 were obtained with the HFs. These results suggest the potential use of texture features for the classification of amyloid PET images. Alzheimer's disease; amyloid PET; florbetapir; fluttemtemamol; Haralick features; grey-level co-occurrence matrix; texture analysis; SUVR +
Footnote †: journal: Medical Imaging  
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standard/European Association of Nuclear Medicine (EANM) practice guideline [8]. The main feature for the visual classification is the grey-to-white matter contrast in amyloid PET images. Cases of abnormally elevated cortical A\(\beta\) deposition are considered amyloid-positive (A\(\beta+\)). The difference between both regions is difficult to identify and the image intensities are more homogeneous. Amyloid-negative (A\(\beta-\)) cases are characterized by slight white matter uptake without grey matter uptake. White matter can be clearly differentiated and the image intensity distribution between both tissues is heterogeneous. The visual analysis and classification of amyloid PET images are recommended by the manufacturers and are performed in clinical practice. It must be noted that amyloid PET imaging is currently only considered a diagnostic biomarker and the corresponding radiotracers have been approved for clinical use for visual interpretation and to classify images as A\(\beta+\) or A\(\beta-\)[1,9,10]. The sensitivity of the visual analysis of amyloid PET images when differentiating healthy control subjects from those with mild cognitive impairment (MCI) or AD is over 90% [11]. However, borderline cases that are difficult to classify visually could require quantitative analysis. Thresholds for the classification between amyloid-positive and amyloid-negative based on the standardized uptake value (SUV) have been calculated for all A\(\beta\)-binding tracer, namely [11C]PiB [12], [18F]FBP [13], [18F]FBB [5] and [18F]FMM [14]. The progression of amyloid deposition as seen in PET images is also studied and its relationship to cognitive functions [15]. Even though the quantitative analysis of A\(\beta\)-binding tracer uptake in PET images is focused on the SUV, visual analysis is still the gold standard in clinical settings [8]. Moreover, it presents limitations and other approaches have previously been proposed. These are mainly motivated by finding an alternative to the reference region normalization, which has shown high variability in longitudinal studies [16,17] and its optimization has been studied for different radiotracers [18, 19, 20].