Highly accelerated submillimeter resolution 3D GRASE with controlled T-2 blurring in T-2-weighted functional MRI at 7 Tesla: A feasibility study

Purpose: To achieve highly accelerated submillimeter resolution T-2-weighted functional MRI at 7T by developing a three-dimensional gradient and spin echo imaging (GRASE) with inner-volume selection and variable flip angles (VFA). Methods: GRASE imaging has disadvantages in that (a) k-space modulation causes T-2 blurring by limiting the number of slices and (b) a VFA scheme results in partial success with substantial SNR loss. In this work, accelerated GRASE with controlled T-2 blurring is developed to improve a point spread function (PSF) and temporal signal-to-noise ratio (ISNR) with a large number of slices. To this end, the VFA scheme is designed by minimizing a trade-off between SNR and blurring for functional sensitivity, and a new GRASE-optimized random encoding, which takes into account the complex signal decays of T-2 and T-2* weightings, is proposed by achieving incoherent aliasing for constrained reconstruction. Numerical and experimental studies were performed to validate the effectiveness of the proposed method over regular and VFA GRASE (R- and V-GRASE). Results: The proposed method, while achieving 0.8 mm isotropic resolution, functional MRI compared to R- and V-GRASE improves the spatial extent of the excited volume up to 36 slices with 52%-68% full width at half maximum (FWHM) reduction in PSF but approximately 2- to 3-fold mean tSNR improvement, thus resulting in higher BOLD activations. Conclusions: We successfully demonstrated the feasibility of the proposed method in T-2-weighted functional MRI. The proposed method is especially promising for cortical layer-specific functional MRI.

Automated summary

A new accelerated GRASE method is developed to improve PSF and tSNR, achieving higher spatial extent and BOLD activations in functional MRI compared to traditional methods. This method shows promise for cortical layer-specific functional MRI applications.