Accelerated Real-time Cine and Flow under In-magnet Staged Exercise

Background: Cardiovascular magnetic resonance imaging (CMR) is a well-established imaging tool for diagnosing and managing cardiac conditions. The integration of exercise stress with CMR (ExCMR) can enhance its diagnostic capacity. Despite recent advances in CMR technology, ExCMR remains technically challenging due to motion artifacts and limited spatial and temporal resolution. Methods: This study investigates the feasibility of biventricular functional and hemodynamic assessment using real-time (RT) ExCMR during a staged exercise protocol in 26 healthy volunteers. We introduce a coil reweighting technique to minimize motion artifacts. In addition, we identify and analyze heartbeats from the end-expiratory phase to enhance the repeatability of cardiac function quantification. To demonstrate clinical feasibility, qualitative results from five patients are also presented. Results: Our findings indicate a consistent decrease in end-systolic volume (ESV) and stable end-diastolic volume (EDV) across exercise intensities, leading to increased stroke volume (SV) and ejection fraction (EF). Coil reweighting effectively reduces motion artifacts, improving image quality in both healthy volunteers and patients. The repeatability of cardiac function parameters, demonstrated by scan-rescan tests in nine volunteers, improves with the selection of end-expiratory beats. Conclusions: The study demonstrates that RT ExCMR with in-magnet exercise is a feasible and effective method for dynamic cardiac function monitoring during exercise. The proposed coil reweighting technique and selection of end-expiratory beats significantly enhance image quality and repeatability.

Motion-robust free-running cardiovascular MRI

PURPOSE: To present and validate an outlier rejection method that makes free-running cardiovascular MRI (CMR) more motion robust. METHODS: The proposed method, called compressive recovery with outlier rejection (CORe), models outliers as an auxiliary variable that is added to the measured data. We enforce MR physics-guided group-sparsity on the auxiliary variable and jointly estimate it along with the image using an iterative algorithm. For validation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and another outlier rejection method using two simulation studies. Then, CORe is compared to CS using five 3D cine and ten rest and stress 4D flow imaging datasets. RESULTS: Our simulation studies show that CORe outperforms CS, RR, and the outlier rejection method in terms of normalized mean squared error (NMSE) and structural similarity index (SSIM) across 50 different realizations. The expert reader evaluation of 3D cine images demonstrates that CORe is more effective in suppressing artifacts while maintaining or improving image sharpness. The flow consistency evaluation in 4D flow images show that CORe yields more consistent flow measurements, especially under exercise stress. CONCLUSION: An outlier rejection method is presented and validated using simulated and measured data. This method can help suppress motion artifacts in a wide range of free-running CMR applications. CODE: MATLAB implementation code is available on GitHub at https://github.com/syedmurtazaarshad/motion-robust-CMR