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.