A 3D convolutional neural network to classify subjects as Alzheimer's disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET.

With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism.

Using EQ·PET to reduce reconstruction-dependent variations in [F]FDG-PET brain imaging.

This study aims at assessing whether EANM harmonisation strategy combined with EQ·PET methodology could be successfully applied to harmonize brain 2-deoxy-2[F]fluoro-D-glucose ([F]FDG) positron emission tomography (PET) images. The NEMA NU 2 body phantom was prepared according to the EANM guidelines with an [F]FDG solution. Raw PET phantom data were reconstructed with three different reconstruction protocols frequently used in clinical PET brain imaging: ([Formula: see text]) Ordered subset expectation maximization (OSEM) 3D with time of flight (TOF), 2 iterations and 21 subsets; ([Formula: see text]) OSEM 3D with TOF, 6 iterations and 21 subsets; and ([Formula: see text]) OSEM 3D with TOF, point spread function (PSF), and 8 iterations and 21 subsets.

Arterial Phase 18F-Fluorocholine PET/CT in Hepatocellular Carcinoma.

A 57-year-old man was referred to our institution for F-fluorocholine PET/CT to characterize a pulmonary nodule in a context of hepatocellular carcinoma. F-FDG PET/CT did not show any uptake of the pulmonary nodule. F-fluorocholine PET/CT showed high uptake of the pulmonary nodule, confirming its metastatic origin.