Wide applications of differentiable two-player sequential games (e. g., image generation by GANs) have raised much interest and attention of researchers to study efficient and fast algorithms.
Positron emission tomography(PET) image reconstruction is an ill-posed inverse problem and suffers from high level of noise due to limited counts received.
Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators.
After the network was trained, the super-resolution (SR) image was generated by supplying the upsampled LR ASL image and corresponding T1-weighted image to the generator of the last layer.
Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology information.
Positron emission tomography (PET) is widely used for clinical diagnosis.
Our method is built as an end-to-end framework for segmentation and classification.
We project the LiDAR point clouds onto the image plane to generate LiDAR images and feed them into one of the branches of the network.