Sequence adaptive field-imperfection estimation (SAFE): retrospective estimation and correction of $B_1^+$ and $B_0$ inhomogeneities for enhanced MRF quantification
This addresses the need for enhanced MRF quantification in medical imaging by enabling retrospective and prospective correction without additional scans, though it is incremental as it builds on existing deep-learning methods for field correction.
The paper tackled the problem of B1+ and B0 field inhomogeneities reducing accuracy in MRF quantitative parameter estimates by proposing a calibration-free, sequence-adaptive deep-learning framework to estimate and correct these effects for any MRF sequence, demonstrating capability on arbitrary sequences at 3T without prior training data.
$B_1^+$ and $B_0$ field-inhomogeneities can significantly reduce accuracy and robustness of MRF's quantitative parameter estimates. Additional $B_1^+$ and $B_0$ calibration scans can mitigate this but add scan time and cannot be applied retrospectively to previously collected data. Here, we proposed a calibration-free sequence-adaptive deep-learning framework, to estimate and correct for $B_1^+$ and $B_0$ effects of any MRF sequence. We demonstrate its capability on arbitrary MRF sequences at 3T, where no training data were previously obtained. Such approach can be applied to any previously-acquired and future MRF-scans. The flexibility in directly applying this framework to other quantitative sequences is also highlighted.