Auto-calibrating parallel mri technique with distortion-optimal image reconstruction

Abstract

The invention is a new computational method for the formation of magnetic resonance (MR) images. The method utilizes the data acquired by the multiple receiver channels available as parallel imaging hardware on standard MRI scanners to: (i) automatically identify a set of multi-input multi-output (MIMO) systems (e.g., MIMO filter banks) that act as interpolation kernels for acquired MR data sets (that can be subsampled with respect to the Nyquist criterion) without requiring a separate calibration scan; and (ii) use the identified MIMO systems to synthesize MR data sets that can in turn be used to produce high quality images, thereby enabling high quality imaging with fewer data samples than current methods (or equivalently provide higher image quality with the same number of data samples). A unique feature of the present invention is its ability to account for aliasing effects and minimize the associated image distortion by optimally adapting the said MIMO interpolation (image reconstruction) kernels. This ability to image with a reduced number of data samples accelerates the imaging process; hence, overcoming the main shortcoming of MRI compared to other medical imaging modalities.