To develop a technique to non-invasively estimate Stroke Volume (SV) in real-time during Magnetic Resonance Imaging (MRI) clinical workflows and guided interventional procedures using induced Magnetohydrodynamic Voltages (VMHD). VMHD overlays occur on Electrocardiogram (ECG) recordings during MRI exams, induced due to interactions between been aortic blood flow (BF) and the strong static magnetic field of the MRI scanner (B₀) (Fig. 1a) [1]. As a result, VMHD can potentially be used to assess the volume of blood ejected into the aortic arch during each cardiac cycle, providing a beat-to-beat SV estimate while allowing the MRI scanner to perform other imaging tasks. As an alternative to current low-fidelity intra-MRI physiological monitoring techniques, the estimation of VMHD-derived SV would allow for continuous high-fidelity physiological monitoring during MRI workflows and interventional procedures. We hypothesize that from ECG vector-decomposition of the VMHD signal, and subject-specific calibration using a CINE Phase Contrast MRI (PCMR) pre-scan, a VMHD-derived estimate of beat-to-beat SV can be derived for each subject and used to continually assess SV, even during induced stress, leaving the MRI free to perform diagnostic or interventional imaging.In order to derive a subject-specific model of VMHD-derived SV, a PCMR pre-scan of the ascending aorta (30 frames) was performed in 7 healthy subjects (n=7) and two subjects diagnosed with Premature Ventricular Contractions (PVC) (n=2) in a Siemens Trio 3T MRI. 12-lead ECG recordings were acquired from each subject during this calibration phase using an MRI-compatible 12-Lead ECG recording system similar to [2-3]. VMHD voltages at each electrode were extracted through the subtraction of ECGs obtained with the subjects outside and inside the MRI [2]. An inverse Dower transform was used to convert the VMHD traces into a Vectorcardiogram (VCG) frame of reference [4], consisting of three spatial (X, Y, Z) components. Using MRI-derived flow information taken during the PCMR pre-scan, a multiple linear regression (MLR) model (Fig. 1b) was developed individually for each subject, using subject -specific coefficients, to correlate VMHD(t) to blood flow as a function of time. Integration of this derived blood flow was then used to estimate beat-to-beat SV. This process was similarly performed in the subjects diagnosed with PVCs (Fig. 1c) to determine efficacy in arrhythmic beat detection. Following the initial calibration, validation of the equation of fit was performed through Real-Time PCMR (RTPC) scans of each subject to compare VMHD(t)- and MRI-derived SV during a 15 minute cardiac exercise stress test with a 30 minute relaxation period after induced stress. Bland Altman analysis was performed to assess the clinical relevance of results obtained through exercise stress testing and quantify the bias of the measurements.Subject-specific equations were derived to correlate VMHD(t) to BF at rest, and validated using RTPC. An average error of 7.22% and 3.69% in SV estimation, respectively, was found during peak stress, and after complete relaxation. Measured beat-to-beat blood flow time-history derived from RTPC and VMHD were highly correlated using a Spearman Rank Correlation Coefficient during stress tests (0.89) and after stress relaxation (=0.86). VMHD-derived arrhythmic beats were detected in the PVC subjects, and an 18.8% decrease in SV was observed during arrhythmic beats as compared to normal beats (Fig. 1d). From the results in a Bland-Altman analysis (Fig. 1e-f), taken from subjects in each stage of exercise stress testing, it is observed that the variability does not appear in a clear trend, and the error is not necessarily dependent on the average. The mean bias line calculated from SV estimation yields a value of 3.3 mL, with an average error of 6.71% calculated from the mean difference and the limits of agreement, a comparable error to those observed in conventional methods used in clinical practice. Accurate beat-to-beat SV estimates were obtained from induced VMHD extracted from 12-lead ECGs recorded and calibrated using PCMR-derived subject specific MLR parameters inside the MRI. This method provides a means for enhanced physiological monitoring during MR imaging and interventional procedures.