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$B_1^+$ and $B_0$ field-inhomogeneities can significantly reduce accuracy androbustness of MRF's quantitative parameter estimates. Additional $B_1^+$ and$B_0$ calibration scans can mitigate this but add scan time and cannot beapplied retrospectively to previously collected data. Here, we proposed acalibration-free sequence-adaptive deep-learning framework, to estimate andcorrect for $B_1^+$ and $B_0$ effects of any MRF sequence. We demonstrate itscapability on arbitrary MRF sequences at 3T, where no training data werepreviously obtained. Such approach can be applied to any previously-acquiredand future MRF-scans. The flexibility in directly applying this framework toother quantitative sequences is also highlighted.

Sequence adaptive field-imperfection estimation (SAFE): retrospective  estimation and correction of $B_1^+$ and $B_0$ inhomogeneities for enhanced  MRF quantification