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Abstract #4762

A Generative Approach to Estimating Coil Sensitivities from Autocalibration data

Yael Balbastre1, Julio Acosta-Cabronero1, Nad├Ęge Corbin1, Oliver Josephs1, John Ashburner1, and Martina F Callaghan1

1Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom

We present an algorithm for inferring sensitivities from low-resolution data, acquired either as an external calibration scan or as an autocalibration region integrated into an under-sampled acquisition. The sensitivity profiles of each coil, together with an unmodulated image common to all coils, are defined as penalised-maximum-likelihood parameters of a generative model of the calibration data. The model incorporates a smoothness constraint for the sensitivities and is efficiently inverted using Gauss-Newton optimization. Using both simulated and acquired data, we demonstrate that this approach can successfully estimate complex coil sensitivities over the full (FOV), and subsequently be used to unfold aliased images.

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