Artificial intelligence in cardiac magnetic resonance fingerprinting

Area of Science:

• Medical Imaging
• Quantitative MRI
• Artificial Intelligence in Medicine

Background:

• Magnetic Resonance Fingerprinting (MRF) enables rapid, multiparametric tissue characterization in a single MRI acquisition.
• Cardiac MRF is particularly promising for simultaneous myocardial T1 and T2 mapping within a single breath-hold.
• Current research focuses on improving MRF accuracy, efficiency, and robustness, but faces computational challenges.

Purpose of the Study:

• To review the application of artificial intelligence (AI) in cardiac Magnetic Resonance Fingerprinting (MRF).
• To highlight how AI addresses computational bottlenecks in MRF sequence optimization, dictionary generation, and image reconstruction.
• To discuss the potential of AI to improve the clinical utility of cardiac MRF.

Main Methods:

• Review of recent advancements in AI, specifically deep learning and neural networks, applied to cardiac MRF.
• Focus on AI's role in optimizing MRF candidate sequences.
• Examination of machine learning approaches for accelerating dictionary generation and image reconstruction.

Main Results:

• AI techniques, including machine learning, have demonstrated the potential to reduce dictionary generation and reconstruction times by orders of magnitude.
• AI can optimize MRF sequences and decrease computational demands for reconstruction and post-processing.
• These AI applications effectively address key bottlenecks in the MRF workflow.

Conclusions:

• AI offers a powerful solution to overcome the computational complexity and processing time limitations of cardiac MRF.
• The integration of AI is crucial for enhancing the efficiency and practical applicability of cardiac MRF.
• AI-driven improvements pave the way for the routine clinical adoption of advanced cardiac MRF techniques.