Respiratory rate estimation by using realignment data from a 1-minute high-temporal-resolution MRI sequence: A feasibility study

Background Breath-hold triggered functional magnetic resonance imaging (bh-fMRI) is a widely available, easily implementable and inexpensive method to quantify cerebrovascular reactivity. Estimation of patients’ respiratory rate is relevant, as it might influence bh-fMRI data quality, but respiratory sensors integrated in the scanner table are not always available. Purpose To propose an approach to estimate patients’ respiratory rate using realignment data of a 1-min high-temporal-resolution cerebral MRI sequence. Material and Methods In a prospective study, 15 patients with Moyamoya angiopathy underwent a 1-min MRI sequence during routine clinical MRI. The respiratory rate was obtained by analyzing patients’ head motion from image realignment data. To obtain robust results, the respiratory rate was calculated using a multi-method approach, incorporating power spectral density and fast Fourier transform and weighted data from multiparametric rigid body transformation. The respiratory rate detected from respiratory sensors integrated in the scanner table was used as reference. Bland–Altman analysis was performed and intra-class correlation coefficients (ICCs) were calculated to compare the respiratory rates. Results Bland–Altman analysis revealed a mean bias of 0.5 breaths/min and limits of agreement (LoA) ranging from −5.3 to 4.4 breaths/min, with no outliers. The overall agreement between the respiratory rate calculated from realignment data and from scanner sensor data was excellent (ICC = 0.866, 95% confidence interval = 0.607–0.955; P < 0.001). Conclusion The calculated mean bias and LoA appear to be clinically acceptable. Estimating respiratory rate from a 1-min high-temporal-resolution MRI sequence may be a feasible method when respiratory sensors integrated in the scanner are not available.