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Abstract #2808

Parameter-free Parallel Imaging and Compressed Sensing

Jonathan I Tamir1, Frank Ong1, Shreyas S Vasanawala2, and Michael Lustig1

1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

We demonstrate an end-to-end parallel imaging and compressed sensing reconstruction that does not rely on parameter tuning. We combine noise pre-whitening, auto-tuned coil sensitivity estimation, and a noise-constrained compressed sensing reconstruction to eliminate the need to select parameters such as soft threshold regularization. The method is validated across a large corpus of phantom and in vivo data at different levels of SNR and with different types of coils in 2D and in 3D. An end-to-end reconstruction is shown for 2D variable density single-shot fast spin-echo with reconstruction times of less than one minute.

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