Myocardial arterial spin labeling with double inversion recovery for reduced physiological noise - Božić-Iven, Maša; Rapacchi, Stanislas; Zhang, Yi; Tao, Qian; Schad, Lothar R.; Weingärtner, Sebastian (Magnetic resonance in medicine, 2025)
Purpose To introduce Double Inversion Recovery (DIR) preparations for myocardial Arterial Spin Labeling (myoASL) for mitigation of heart rate (HR) variability induced physiological noise (PN). Methods DIR-labeling was implemented for double ECG-gated myoASL-sequences and compared with conventional Flow-sensitive Alternating Inversion Recovery (FAIR) labeling using single inversions. In DIR-preparations, the FAIR-inversion pulses were immediately followed by an identical reinversion pulse, applied either slice-selectively or nonselectively. Bloch-equation-based simulation and phantom experiments were performed to evaluate the PN and SNR across a range of HR variabilities. Data from six healthy subjects were acquired to evaluate myocardial blood flow (MBF), PN, and SNR in vivo. Results Simulation experiments showed that the average MBF values remained nearly constant across the range of HR variabilities and were comparable across all three sequences. However, DIR-labeling allowed for greater recovery of the myocardial background signal, which mitigates the sensitivity to HR-dependent changes in the inversion time. Consequently, PN in the presence of HR variability was substantially reduced with DIR-labeling. For HR variabilities corresponding to the mean value observed in vivo, this resulted in a simulated SNR gain of 1.79 \ ± \ 0.90 for selective and 1.55 \ ± \ 0.77 for nonselective DIR-labeling. In vivo, DIR-labeling showed reduced PN, with 53% (\ p<0.05 \)/44% (\ p=0.16 \) less PN compared with conventional FAIR-myoASL, leading to an average SNR gain of 1.47 \ ± \ 0.63 (\ p=0.09 \)/1.32 \ ± \ 0.57 (\ p=0.84 \) with selective/nonselective reinversions. Conclusion The proposed DIR-preparations reduce sensitivity to HR variations and alleviate PN in double ECG-gated myoASL, improving the precision of myoASL-based perfusion quantification.