Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing

We show how to sequentially optimize magnetic resonance imaging measurement designs over stacks of neighbouring image slices, by performing convex variational inference on a large scale non-Gaussian linear dynamical system, tracking dominating directions of posterior covariance without imposing any factorization constraints. Our approach can be scaled up to high-resolution images by reductions to numerical mathematics primitives and parallelization on several levels. In a first study, designs are found that improve significantly on others chosen independently for each slice or drawn at random.


Published in:
Proceedings of the 23rd Annual Conference on Neural Information Processing Systems, 1633-1641
Presented at:
Neural Information Processing Systems 22, Vancouver, BC
Year:
2010
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 Record created 2010-12-01, last modified 2018-03-17

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