Patient-independent, MHD-robust R-peak detection for retrospective gating in cardiac MRI imaging.

Objective.In cardiovascular magnetic resonance imaging, synchronization of image acquisition with heart motion (calledgating) is performed by detecting R-peaks in electrocardiogram (ECG) signals. Effective gating is challenging with 3T and 7T scanners, due to severe distortion of ECG signals caused by magnetohydrodynamic effects associated with intense magnetic fields. This work proposes an efficient retrospective gating strategy that requires no prior training outside the scanner and investigates the optimal number of leads in the ECG acquisition set.Approach.The proposed method was developed on a data set of 12-lead ECG signals acquired within 3T and 7T scanners. Independent component analysis is employed to effectively separate components related with cardiac activity from those associated to noise. Subsequently, an automatic selection process identifies the components best suited for accurate R-peak detection, based on heart rate estimation metrics and frequency content quality indexes.Main results.The proposed method is robust to different B0 field strengths, as evidenced by R-peak detection errors of 2.4 ± 3.1 ms and 10.6 ± 15.4 ms for data acquired with 3T and 7T scanners, respectively. Its effectiveness was verified with various subject orientations, showcasing applicability in diverse clinical scenarios. The work reveals that ECG leads can be limited in number to three, or at most five for 7T field strengths, without significant degradation in R-peak detection accuracy.Significance.The approach requires no preliminary ECG acquisition for R-peak detector training, reducing overall examination time. The gating process is designed to be adaptable, completely blind and independent of patient characteristics, allowing wide and rapid deployment in clinical practice. The potential to employ a significantly limited set of leads enhances patient comfort.

Accurate hemodynamic response estimation by removal of stimulus-evoked superficial response in fNIRS signals.

Objective.We address the problem of hemodynamic response (HR) estimation when task-evoked extra-cerebral components are present in functional near-infrared spectroscopy (fNIRS) signals. These components might bias the HR estimation; therefore, careful and accurate denoising of data is needed.Approach.We propose a dictionary-based algorithm to process each single event-related segment of the acquired signal for both long separation (LS) and short separation (SS) channels. Stimulus-evoked components and physiological noise are modeled by means of two distinct waveform dictionaries. For each segment, after removal of the physiological noise component in each channel, a template is employed to estimate stimulus-evoked responses in both channels. Then, the estimate from the SS channel is employed to correct the evoked superficial response and refine the HR estimate from the LS channel.Main results.Analysis of simulated, semi-simulated and real data shows that, by averaging single-segment estimates over multiple trials in an experiment, reliable results and improved accuracy compared to other methods can be obtained. The average estimation error of the proposed method for the semi-simulated data set is 34% for oxy-hemoglobin (HbO) and 78% for deoxy-hemoglobin (HbR), considering 40 trials. The proposed method outperforms the results of the methods proposed in the literature. While still far from the possibility of single-trial HR estimation, a significant reduction in the number of averaged trials can also be obtained.Significance.This work proves that dedicated dictionaries can be successfully employed to model all different components of fNIRS signals. We demonstrate the effectiveness of a specifically designed algorithm structure in dealing with a complex denoising problem, enhancing the possibilities of fNIRS-based HR analysis.