Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic Resonance

TL;DR

This paper reviews recent deep learning techniques for improving cardiac magnetic resonance image reconstruction, highlighting their advantages over traditional methods and discussing current challenges and future prospects.

Contribution

It provides a comprehensive overview of supervised deep learning methods applied to cardiac MRI reconstruction, emphasizing recent advances and identifying ongoing challenges.

Findings

DL methods outperform traditional reconstruction in quality and speed

Supervised DL techniques are effective for cardiac MRI reconstruction

Challenges include data limitations and model generalization issues

Abstract

Artificial intelligence (AI) and Machine Learning (ML) have shown great potential in improving the medical imaging workflow, from image acquisition and reconstruction to disease diagnosis and treatment. Particularly, in recent years, there has been a significant growth in the use of AI and ML algorithms, especially Deep Learning (DL) based methods, for medical image reconstruction. DL techniques have shown to be competitive and often superior over conventional reconstruction methods in terms of both reconstruction quality and computational efficiency. The use of DL-based image reconstruction also provides promising opportunities to transform the way cardiac images are acquired and reconstructed. In this chapter, we will review recent advances in DL-based reconstruction techniques for cardiac imaging, with emphasis on cardiac magnetic resonance (CMR) image reconstruction. We mainly focus on supervised DL methods for the application, including image post-processing techniques, model-driven approaches and k-space based methods. Current limitations, challenges and future opportunities of DL for cardiac image reconstruction are also discussed.