Advanced Cardiac MRI
Integrating AI and Machine Learning for Post Imaging Reconstruction
Archana Vadiraj Malagi (Cedars-Sinai Medical Center, Los Angeles)
Xinqi Li (Cedars-Sinai Medical Center, Los Angeles)
Qian Tao (TU Delft - ImPhys/Tao group)
Hsin Jung Yang (Cedars-Sinai Medical Center, Los Angeles)
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Abstract
Purpose of Review: This review explores the advancements in deep learning (DL)-based cardiac magnetic resonance (CMR) reconstruction, focusing on its role in accelerating imaging, denoising, super-resolution, motion artifact correction, and quantitative mapping. It highlights the transition from parallel imaging and compressed sensing to artificial intelligence (AI)-driven approaches that enhance image quality and diagnostic accuracy. Recent Findings: Supervised and self-supervised DL models can significantly reduce scan times, enabling high-fidelity reconstructions from undersampled data. Generative adversarial network (GAN)-based super-resolution techniques enhance spatial resolution, while denoising networks improve signal-to-noise ratio. Motion correction strategies, including spatiotemporal learning, have enhanced free-breathing acquisitions. Physics-guided models incorporate MRI signal constraints for improved T1/T2 mapping and myocardial tissue characterization. Summary: DL-driven CMR reconstruction optimizes imaging speed, quality, and artifact suppression. Despite challenges in dataset standardization and clinical validation, AI is advancing real-time, high-fidelity CMR, facilitating broader clinical adoption.
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