The Accuracy of Noise Covariance Estimation & Its Relationship with Signal-To-Noise Ratio in Parallel Magnetic Resonance Imaging

Yu Ding¹, Yiu-Cho Chung², Orlando Simontetti

Image based parallel magnetic resonance imaging (pMRI) techniques (SENSE or its variants) use the best linear unbiased estimation (BLUE) to reconstruct image. Mathematically, the signal-to-noise-ratio (SNR) of images reconstructed by BLUE is better or equal to the SNR of the simple least square solution, depending on the accuracy of the noise covariance matrix used. Hence, the accuracy of the noise covariance matrix estimation affects the SNR performance of pMRI. In this study, we use volunteer study to quantify the how the errors of covariance matrix estimation affect image SNR, and propose a guideline for accurate covariance matrix estimation.