  While Positron emission tomography (PET) imaging has been widely used in
diagnosis of number of diseases, it has costly acquisition process which
involves radiation exposure to patients. However, magnetic resonance imaging
(MRI) is a safer imaging modality that does not involve patient's exposure to
radiation. Therefore, a need exists for an efficient and automated PET image
generation from MRI data. In this paper, we propose a new frequency-aware
attention U-net for generating synthetic PET images. Specifically, we
incorporate attention mechanism into different U-net layers responsible for
estimating low/high frequency scales of the image. Our frequency-aware
attention Unet computes the attention scores for feature maps in low/high
frequency layers and use it to help the model focus more on the most important
regions, leading to more realistic output images. Experimental results on 30
subjects from Alzheimers Disease Neuroimaging Initiative (ADNI) dataset
demonstrate good performance of the proposed model in PET image synthesis that
achieved superior performance, both qualitative and quantitative, over current
state-of-the-arts.
