Noise Reduction in Multiple Echo Data Sets using Singular Value Decomposition
Bydder M, Du J
University of California
Noise Reduction in Multiple Echo Data Sets using Singular Value DecompositionA method is described for de-noising multiple echo data sets using singular value decomposition (SVD). Images are acquired using a multiple gradient or spin echo sequence and the variation of the signal with echo time (TE) in all pixels is subjected to SVD to categorize the components of the signal variation. The least significant components, which tend to characterize noise variation, are suppressed to reduce their contribution. The result of this procedure is a reduction in noise in the individual TE images. A minimum variance filter is used to optimally approximate the noise-free images.