Dynamic arterial spin labeling (dASL) images showed the existence of large-scale structured noise, which violates the Gaussian assumptions of baseline functional imaging studies. Here, we evaluated the performance of two deep neural network (DNN) methods on removing the structured noise of ASL images, using the simulated data and real image data. The DNN model, with the noise structure learned and incorporated, demonstrates consistently improved performance compared to the DNN model without the explicitly incorporated noise structure. These results indicate that the noise structure incorporated DNN model is promising in removing the structured noise from the ASL functional images.
This abstract and the presentation materials are available to members only; a login is required.