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.

Myocardial, Retro-orbital, and Bilateral Testicular Metastases of an Ileal Neuroendocrine Tumor in 68 Ga-DOTATATE PET/CT.

A 71-year-old man with a newly discovered metastatic grade II neuroendocrine tumor of the terminal ileum was referred for a 68 Ga-DOTATATE PET/CT scan to stage the disease and assess suitability for PRRT (peptide receptor radionuclide therapy). The patient was known to have secondary nodal and bone/liver metastatic disease through prior morphological investigations. PET images revealed an atypical pattern of metastatic disease, showcasing secondary lesions in bilateral extraocular muscles, the myocardium, and both testes.

Optimization of Automated Radiosynthesis of Gallium-68-Labeled PSMA11 with Two [Ge]Ge/[Ga]Ga Generators: Fractional Elution or Prepurification?

Prostate cancer is one of the most common forms of cancer in men. An imaging technique for its diagnosis is [Ga]-prostate-specific membrane antigen ([Ga]Ga-PSMA-11) positron emission tomography (PET). To address the increasing demand for [Ga]-labeled peptides and reduce the cost of radiosynthesis, it is therefore necessary to optimize the elution process of [Ge]Ge/[Ga]Ga generators.