Physics in Medicine & Biology
Machine learning for predicting full-count FDG PET brain images from low-count acquisitions in suspected dementia: a clinical and quantitative evaluation
Abstract Objective. Artificial intelligence methods for denoising low-count (LC) FDG positron emission tomography (PET) brain images are usually evaluated using image quality metrics alone, with limited direct clinical assessment, particularly in suspected dementia. This study evaluated a machine-learning image quality transfer (IQT) method for predicting full-count FDG PET brain images from LC acquisitions using both quantitative metrics and blinded clinical assessment. Approach . Forty-one FDG PET/CT patients wit …