The subject invention relates to a method for reconstructing a dynamic
image series. Embodiments of the subject invention can be considered and / or referred to as a
parallel imaging-prior-information imaging (parallel-prior)
hybrid method. A specific embodiment can be referred to as k-t GRAPPA. The subject method can involve linear interpolation of data in k-t space. Linear interpolation of
missing data in k-t space can
exploit the correlation of the acquired data in both k-space and time. Several extra auto-calibration
signal (ACS) lines can be acquired in each k-space scan and the correlation of the acquired data can be calculated based on the extra ACS lines. In an embodiment, ACS lines can be calculated based on other acquired data, such that values in an ACS line can be partially acquired and the unacquired values can be calculated and filled in based on the acquired values. In a preferred embodiment, no extra training data is used and no sensitivity map is used. In an embodiment, the extra ACS lines can be directly applied in the k-space to improve the
image quality. Because the correlations exploited via the subject method are local and intrinsic, the subject method does not require that the sensitivity maps have no change during the acquisition. Advantageously, the subject method can be utilized when sensitivity maps change, preferably slowly, during the acquisition of the data.