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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.