Abstract
In this article we aim at improving the performance of whole brain functional imaging at very high temporal resolution (100 ms or less). This is achieved by utilizing a nonlinear regularized parallel image reconstruction scheme, where the penalty term of the cost function is set to the L1-norm measured in some transform domain. This type of image reconstruction has gained much attention recently due to its application in compressed sensing and has proven to yield superior spatial resolution and image quality over e.g. Tikhonov regularized image reconstruction. We demonstrate that by using nonlinear regularization it is possible to more accurately localize brain activation from highly undersampled k-space data at the expense of an increase in computation time.
Highlights
Conventional functional magnetic resonance imaging is performed using multi-slice EPI with TR of 2–3 s
The strongly undersampled trajectories used in these studies lead to very high undersampling factors and prohibit a conventional non-cartesian image reconstruction using e.g. SENSE [5,6] or other parallel imaging methods
Tikhonov regularization [7,8] was previously employed to find a sensible solution to the ill-conditioned reconstruction problem
Summary
Conventional functional magnetic resonance imaging (fMRI) is performed using multi-slice EPI with TR of 2–3 s. Tikhonov regularization [7,8] was previously employed to find a sensible solution to the ill-conditioned reconstruction problem Another reconstruction approach for highly undersampled data was taken by Lin et al, where they used reconstruction techniques usually found in radar and magnetoencephalography literature [9]. Lee et al have shown that by using interleaved data acquisition, a single channel coil, density compensated non-uniform Fourier transformation and UNFOLD [11,12], the temporal resolution of an fMRI experiment can be increased Their approach relies on temporal filtering of the reconstructed data, which can potentially affect physiological signal components (BOLD or otherwise). Nonlinear regularization techniques have gained strong attention over the last few years in MRI, since they have the potential to yield better image quality when compared to linearly regularized approaches with an equal amount of k-space data [14,15]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
