Fast algorithms for GS-model-based image reconstruction in data-sharing Fourier imaging
Abstract
Many imaging experiments involve acquiring a time series of images. To improve imaging speed, several "data-sharing" methods have been proposed, which collect one (or a few) high-resolution reference(s) and a sequence of reduced data sets. In image reconstruction, two methods, known as "Keyhole" and reduced-encoding imaging by generalized-series reconstruction (RIGR), have been used. Keyhole fills in the unmeasured high-frequency data simply with those from the reference data set(s), whereas RIGR recovers the unmeasured data using a generalized series (GS) model, of which the basis functions are constructed based on the reference image(s). This correspondence presents a fast algorithm (and two extensions) for GS-based image reconstruction. The proposed algorithms have the same computational complexity as the Keyhole algorithm, but are more capable of capturing high-resolution dynamic signal changes.
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