A superresolution framework for fMRI sequences and its impact on resulting activation maps

This paper investigates the benefits of using a superresolution approach for fMRI sequences in order to obtain high-quality activation maps based on low-resolution acquisitions. We propose a protocol to acquire low-resolution images, shifted in the slice direction, so that they can be used to generate superresolution images. Adopting a variational framework, the superresolution images are defiend as the minimizers of objective functions. We focus on edge preserving regularized objective functions because of their ability to preserve details and edges. We show that applying regularization only in the slice direction leads more pertinent solutions than 3-dimensional regularization. Moreover, it leads to a considerably easier optimization problem. The latter point is crucial since we have to process long fMRI sequences. The solutions - the sought high resoltion images - are calculated based on a half-quadratic reformulation of the objective function which allows fast minimization schemes to be implemented. Our acquisition protocol and processing technique are tested both on simulated and real functional MRI datasets.