In Magnetic Resonance Fingerprinting, the accuracy of the results is dominated by undersampling artifacts. While in classical relaxometry techniques, the omission of data always leads to a larger error, in fingerprinting, undersampling artifacts can lead to both an increase or a decrease in error. The “temporal encoding efficiency” of fingerprinting can be analyzed based on the change in matching error upon omission of a single time point (leave-one-out). We propose a first-order perturbation of the undersampling error to visualize and identify temporal sequence segments of primary parameter encoding and apply these insights to shorten an exemplary MRF sequence by truncation.
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