| 摘要 |
A method for use in magnetic resonance imaging, or other imaging modality, wherein dynamic data is acquired in which the signal intensity of each pixel has a repetitive nature or in which (a subset of) the pixels have a common line shape. The common line shape of a data set comprising a plurality of signal intensity time curves is estimated by performing principal component analysis (PCA) in respect of the data set using a singular value decomposition (SVD) algorithm, wherein the first principal component is representative of the common line shape. Then, a time shift value is obtained using the common line shape and one or more of its derivatives and linear regression and applied to the respective signal intensity time curves to correct for random temporal shift in the repetitive elements. |
