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Abstract #3734

A Bilinear Noise Transfer Model for SENSE Reconstruction

Yu Ding1, Rizwan Ahmad2, Hui Xue3, Lee C. Potter2, Samuel T. Ting2, Ning Jin4, Orlando P. Simonetti2

1The Ohio State University, Columbus, OH, United States; 2The Ohio State University, Columbus,, OH, United States; 3Corporate Research, Siemens Corporation, Princeton, NJ, United States; 4Siemens 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.