A Generalized Series Approach to Sparsely-Sampled fMRI

Hien Nguyen¹, Gary H. Glover¹

In high resolution functional MRI, it is often desirable to reduce the readout duration to make the acquired data less prone to T2* susceptibility artifacts at the expense of SNR. This can be achieved by undersampling k-space. However, the conventional Fourier transform-based reconstruction method suffers from undersampling artifacts such as high-frequency ringing and loss of resolution. In this work we propose a new imaging approach to fMRI with under-sampled data by exploiting the generalized series constraint in the penalized maximum-likelihood framework. The effectiveness of the method is characterized and illustrated by experiments at 3T.