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Abstract #2417

Automatic Coil Compression for Parallel MRI based on Noise Variance Estimation

Allan Raventos 1 , Tao Zhang 1 , and John M. Pauly 1

1 Electrical Engineering, Stanford University, Stanford, California, United States

Coil compression methods combine parallel MRI data from large coil arrays into few virtual coils, and therefore significantly speed up the reconstruction. Coil compression is usually achieved by singular value decomposition, where the number of virtual coils can be determined by thresholding the singular values. However, the thresholds have to be manually tuned for different datasets or coil geometries. Here, a new approach based on noise variance estimation is proposed to automatically select the number of virtual coils. The proposed method is validated on datasets from different coil geometries.

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