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Abstract #3826

L1-Regularized GRAPPA Kernel Estimate

Yu Ding1, Rizwan Ahmad1, Hui Xue2, Samuel T. Ting1, Ning Jin3, Orlando P. Simonetti1

1The Ohio State University, Columbus,, OH, United States; 2Corporate Research, Siemens Corporation, Princeton, NJ, United States; 3Siemens Healthcare, Columbus,, OH, United States

SENSE is a widely used parallel imaging technique. The so-called g-factor represents how noise in the raw data affects the noise in the reconstructed image. However, the g-factor calculation does not take into account the noise in the channel sensitivity map. In this abstract, we present a noise transfer model in SENSE reconstruction that takes into account the noise in both the raw data and the channel sensitivity map. A phantom study showed that the model has satisfactory accuracy. We observed that a large portion (> 35%) of the image noise originates from the noise in the sensitivity map.