Thimo Grotz1, Benjamin Zahneisen1, Marco Reisert1, Maxim Zaitsev1, Jrgen Hennig1
1Dept. of Diagnostic Radiology, Medical Physics, University Hospital Freiburg, Freiburg, Germany
Standard fMRI experiments have a rather limited temporal resolution of 1-3s. The temporal resolution of fMRI experiments can be increased by an order of magnitude by acquiring less k-space and using a high number of receive channels. Image reconstruction is thus an ill-posed inverse problem. Here we would like to introduce a new approach, based on neural networks, to reconstruct the undersampled fMRI data that offers a significantly improved point spread function with reduced spatial spread and hence improved spatial localization of activation.