Magnetic Resonance Fingerprinting estimates multi-parametric maps from a series of highly undersampled time-point images. However, MRF scan times are still long due to the large amount of time-point images (~1000) required to produce accurate multi-parametric maps. Here we propose to exploit redundant information in time-point images with similar contrast to accelerate the MRF scan by further undersampling each time-point image and/or significantly reducing the number of required images in the series. The proposed approach achieved an acceleration factor of 5.7× compared to conventional undersampled MRF while maintaining parametric map quality.
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