Predictive signal modeling and multi-rate filtering in accelerated cardiac MRI.

purposeTrue real-time cardiac MRI (CMR), necessary for capturing live cardiac dynamics and imaging irregular cardiac rhythms, remains challenging. In this article, we move toward real-time CMR in multiple reconstruction frameworks via strategies to predict cardiac motion, improve computational efficiency, reduce artifacts, and preserve spatial resolution. THEORY AND

methodsA published predictive signal model (PMOT) for imaging irregular cardiac dynamics was modified (mPMOT) to enable efficient computation of state-transition matrices for predicting cardiac motion, as training PMOT is computationally expensive. A multi-rate Kalman filter framework was developed to enable computationally efficient reconstructions of high-resolution, large-matrix CMR datasets. Reconstructions were evaluated on multi-coil CMR data in human and swine using multi-rate Kalman filtering and compressed sensing (CS).

resultsTraining mPMOT is two orders of magnitude faster than PMOT. Across all datasets and frameworks, mPMOT facilitated high-quality reconstructions of CMR images for different undersampling patterns at acceleration factors of 9 and 13.5. Furthermore, mPMOT substantially reduced temporal blurring artifacts naturally present in CS reconstructions. In swine, mPMOT reduced the mean-squared error of the multi-rate Kalman filter by two orders of magnitude. The multi-rate Kalman filter implementation maintained spatial resolution while reducing computation time from 5439 s to 56 s in select applications.

conclusionOur mPMOT is computationally efficient and can be integrated within multiple established reconstruction frameworks to ensure robust tracking and reconstruction for dynamic and real-time CMR applications.