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Abstract #2676

A Generalized Series Approach to Sparsely-Sampled fMRI

Hien Nguyen1, Gary H. Glover1

1Department of Radiology, Stanford University, Palo Alto, CA, United States

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.