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Purpose of Review <p>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.</p> Recent Findings <p>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.</p> Summary <p>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.</p>

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Advanced Cardiac MRI: Integrating AI and Machine Learning for Post Imaging Reconstruction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月11日

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  • Archana Vadiraj Malagi,
  • Xinqi Li,
  • Qian Tao,
  • Hsin-Jung Yang

摘要

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.