Abstract #0479

An Eigen-Vector Approach to AutoCalibrating Parallel MRI, Where SENSE Meets GRAPPA

Michael Lustig¹, Peng Lai², Mark Murphy¹, Shreyas Mark Vasanawala³, Michael Elad⁴, Jian Zhang⁵, John Pauly⁵

¹Electrical Engineering & Computer Science, University of California Berkeley, Berkeley, CA, USA; ²ASL West, GE Healthcare, Menlo Park, CA, USA; ³Radiology, Stanford University, Stanford, CA, USA; ⁴Computer Science, Technion IIT, Haifa, Israel; ⁵Electrical Engineering, Stanford University, Stanford, CA, USA

Parallel imaging techniques can be categorized roughly into two families: explicit sensitivity based methods like SENSE and autocalibrating methods (acPI) like GRAPPA. In this work we finally bridge the gap between these approaches. We present a new way to compute the explicit sensitivity maps that are (implicitly) used by acPI methods. These are found by Eigen-vector analysis of the k-space filtering in acPI algorithms. Our Eigen approach performs like other acPI methods when the prescribed FOV is smaller than the object, i.e., is not susceptible as SENSE to FOV limitations. At the same time, the reconstruction performs optimal calibration and optimal reconstruction, as SENSE. Our approach can be used to find the explicit sensitivity maps of any acPI method from its k-space kernels.