A predictive signal model for dynamic cardiac magnetic resonance imaging.

Robust dynamic cardiac magnetic resonance imaging (MRI) has been a long-standing endeavor-as real-time imaging can provide information on the temporal signatures of disease we currently cannot assess-with the past decade seeing remarkable advances in acceleration using compressed sensing (CS) and artificial intelligence (AI). However, substantial limitations to real-time imaging remain and reconstruction quality is not always guaranteed. To improve reconstruction fidelity in dynamic cardiac MRI, we propose a novel predictive signal model that uses a priori statistics to adaptively predict temporal cardiac dynamics. By using a small training set obtained from the same patient, the new signal model can achieve robust dynamic cardiac MRI in the presence of irregular cardiac rhythm. Evaluation on simulated irregular cardiac dynamics and prospectively undersampled clinical cardiac MRI data demonstrate improved reconstruction quality for two reconstruction frameworks: Kalman filter and CS. The predictive model also works with different undersampling patterns (cartesian, radial, spiral) and can serve as a versatile foundation for robust dynamic cardiac MRI.

Primer and Historical Review on Rapid Cardiac CINE MRI.

Acceleration is an important consideration when imaging moving organs such as the heart. Not only does acceleration enable motion-free scans but, more importantly, it lies at the heart of capturing the dynamics of cardiac motion. For over three decades, various ingenious approaches have been devised and implemented for rapid CINE MRI suitable for dynamic cardiac imaging. Virtually all techniques relied on acquiring less data to reduce acquisition times. Parallel imaging was among the first of these innovations, using multiple receiver coils and mathematical algorithms for reconstruction; acceleration factors of 2 to 3 were readily achieved in clinical practice. However, in the context of imaging dynamic events, further decreases in scan time beyond those provided by parallel imaging were possible by exploiting temporal coherencies. This recognition ushered in the era of k-t accelerated MRI, which utilized predominantly statistical methods for image reconstruction from highly undersampled k-space. Despite the successes of k-t acceleration methods, however, the accuracy of reconstruction was not always guaranteed. To address this gap, MR physicists and mathematicians applied compressed sensing theory to ensure reconstruction accuracy. Reconstruction was, indeed, more robust, but it required optimizing regularization parameters and long reconstruction times. To solve the limitations of all previous methods, researchers have turned to artificial intelligence and deep neural networks for the better part of the past decade, with recent results showing rapid, robust reconstruction. This review provides a comprehensive overview of key developments in the history of CINE MRI acceleration, and offers a unique and intuitive explanation behind the techniques and underlying mathematics.Level of Evidence: 5Technical Efficacy Stage: 1.