Artificial intelligence in cardiac magnetic resonance fingerprinting

Abstract

Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T1 and T2 mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review

“…The workflow comprises ( a ) ECG-triggered MRF acquisition with motion corrected MRF image reconstruction; ( b ) simulating dictionaries corresponding to specific tissue properties by varying the acquisition parameters; ( c ) dictionary matching; and ( d ) the cardiac parametric map reconstructed after dictionary matching. The figure was derived from [ 15 ].…”

“…In [ 14 ], the authors discussed the technical and potential clinical application of MRF in the characterization of cardiomyopathies, tissue characterization in the left atrium and right ventricle, post-cardiac transplantation assessment, reduction in contrast material, pre-procedural planning for electrophysiology interventions, and imaging of patients with implanted devices. In [ 15 ], the authors discussed technical developments at the intersection of artificial intelligence and MRF for cardiac imaging. In [ 16 ], the authors discussed challenges and recent developments in integrating MRF into the radiotherapy pipeline.…”

The future of cardiovascular magnetic resonance: All-in-one vs. real-time (Part 1)

“…The complexity of the acquisition and reconstruction of “all-in-one” processes, along with the need for patient-specific dictionaries have challenged their clinical deployment. However, recent developments in artificial intelligence/deep learning (AI/DL) have been incorporated to overcome some of these challenges [53] . As an example, deep learning has been used to reduce the time needed to generate cardiac MRF dictionaries from 158 s to 0.8 s [54] .…”

Magnetic Resonance Fingerprinting

“…Artificial intelligence offers a promising solution for overcoming several computational bottlenecks in cardiac MRF 149 . Artificial intelligence can be used to accelerate the dictionary generation process in MRF.…”

“…Artificial intelligence offers a promising solution for overcoming several computational bottlenecks in cardiac MRF. 149 Artificial intelligence can be used to accelerate the dictionary generation process in MRF. One approach that has been investigated uses a neural network that receives the cardiac rhythm timings from the ECG signal as an input and outputs the dictionary in under 1 second, thus eliminating the need for a time-consuming Bloch equation simulation.…”