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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