Generalized auto-calibrating technique for image reconstruction from sensitivity encoded MRI data

Abstract

MRI with multiple receiver coils (parallel MRI) has been extensively used to achieve higher spatial and temporal resolution, suppress imaging artifacts, and reduced scan time. A number of techniques have been proposed to reconstruct images from reduced (undersampled) k-space datasets acquired by multiple coils. All the techniques require some type of calibration information to describe image encoding by spatially varying coil sensitivities. This information can be derived from supplementary calibration scans. However, this approach increases scan time and can be erroneous due to patient motion between calibration and imaging scans. Auto-calibrating techniques such as the commonly used GRAPPA, do not require calibration scans and estimate reconstruction coefficients directly from acquired k-space data. GRAPPA typically gives good quality results for low undersampling rates. However, strong noise amplification and non-resolved aliasing artifacts makes the technique less applicable in cases of high undersampling. In this work, we have proposed a novel auto-calibrating technique for image reconstruction from sensitivity encoded MRI data that overcomes limitations of the existing auto-calibrating techniques. In the proposed technique (GARSE), specifics of coil sensitivity representation in the image and k-space domains are utilized in the reconstruction in such a way that more trustworthy reconstruction coefficients can be identified resulting in improved image quality. GARSE reconstruction coefficients are spatially variable and adjusted according to local coil sensitivities characteristics, whereas GRAPPA reconstruction coefficients are spatially invariant and, therefore, sub-optimal. Results from MRI studies of phantoms and humans demonstrate substantial advantages of GARSE in comparison with GRAPPA, especially for high undersampling rates.