Cardiac magnetic resonance fingerprinting: Trends in technical development and potential clinical applications

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

Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.

Cardiac Magnetic Resonance Fingerprinting: Potential Clinical Applications

PURPOSE OF REVIEW

Cardiac magnetic resonance fingerprinting (cMRF) has developed as a technique for rapid, multi-parametric tissue property mapping that has potential to both improve cardiac MRI exam efficiency and expand the information captured. In this review, we describe the cMRF technique, summarize technical developments and in vivo reports, and highlight potential clinical applications.

RECENT FINDINGS

Technical developments in cMRF continue to progress rapidly, including motion compensated reconstruction, additional tissue property quantification, signal time course analysis, and synthetic LGE image generation. Such technical developments can enable simplified CMR protocols by combining multiple evaluations into a single protocol and reducing the number of breath-held scans. cMRF continues to be reported for use in a range of pathologies; however barriers to clinical implementation remain. Technical developments are described in this review, followed by a focus on potential clinical applications that they may support. Clinical translation of cMRF could shorten protocols, improve CMR accessibility, and provide additional information as compared to conventional cardiac parametric mapping methods. Current needs for clinical implementation are discussed, as well as how those needs may be met in order to bring cMRF from its current research setting to become a viable tool for patient care.

Fingerprinting MINOCA: Unraveling Clues With Quantitative CMR

In the following case series, we describe the clinical presentation of 2 patients with myocardial infarction with nonobstructive coronary arteries with different underlying pathophysiologic mechanisms. In both scenarios, cardiac magnetic resonance (CMR) imaging provided comprehensive tissue characterization with both conventional parametric mapping techniques and CMR fingerprinting. These cases demonstrate the diagnostic utility for CMR to elucidate the underlying etiology and appropriate therapeutic strategy. (Level of Difficulty: Advanced.).

Simultaneous multi-parametric acquisition and reconstruction techniques in cardiac magnetic resonance imaging: Basic concepts and status of clinical development

Simultaneous multi-parametric acquisition and reconstruction techniques (SMART) are gaining attention for their potential to overcome some of cardiovascular magnetic resonance imaging's (CMR) clinical limitations. The major advantages of SMART lie within their ability to simultaneously capture multiple "features" such as cardiac motion, respiratory motion, T1/T2 relaxation. This review aims to summarize the overarching theory of SMART, describing key concepts that many of these techniques share to produce co-registered, high quality CMR images in less time and with less requirements for specialized personnel. Further, this review provides an overview of the recent developments in the field of SMART by describing how they work, the parameters they can acquire, their status of clinical testing and validation, and by providing examples for how their use can improve the current state of clinical CMR workflows. Many of the SMART are in early phases of development and testing, thus larger scale, controlled trials are needed to evaluate their use in clinical setting and with different cardiac pathologies.

Characterization of cardiac amyloidosis using cardiac magnetic resonance fingerprinting

BACKGROUND

Cardiac amyloidosis (CA) is an infiltrative cardiomyopathy with poor prognosis absent appropriate treatment. Elevated native myocardial T₁ and T₂ have been reported for CA, and tissue characterization by cardiac MRI may expedite diagnosis and treatment. Cardiac Magnetic Resonance Fingerprinting (cMRF) has the potential to enable tissue characterization for CA through rapid, simultaneous T₁ and T₂ mapping. Furthermore, cMRF signal timecourses may provide additional information beyond myocardial T₁ and T₂.

METHODS

Nine CA patients and five controls were scanned at 3 T using a prospectively gated cMRF acquisition. Two cMRF-based analysis approaches were examined: (1) relaxometric-based linear discriminant analysis (LDA) using native T₁ and T₂, and (2) signal timecourse-based LDA. The Fisher coefficient was used to compare the separability of patient and control groups from both approaches. Leave-two-out cross-validation was employed to evaluate the classification error rates of both approaches.

RESULTS

Elevated myocardial T₁ and T₂ was observed in patients vs controls (T₁: 1395 ± 121 vs 1240 ± 36.4 ms, p < 0.05; T₂: 36.8 ± 3.3 vs 31.8 ± 2.6 ms, p < 0.05). LDA scores were elevated in patients for relaxometric-based LDA (0.56 ± 0.28 vs 0.18 ± 0.13, p < 0.05) and timecourse-based LDA (0.97 ± 0.02 vs 0.02 ± 0.02, p < 0.05). The Fisher coefficient was greater for timecourse-based LDA (60.8) vs relaxometric-based LDA (1.6). Classification error rates were lower for timecourse-based LDA vs relaxometric-based LDA (12.6 ± 24.3 vs 22.5 ± 30.1%, p < 0.05).

CONCLUSIONS

These findings suggest that cMRF may be a valuable technique for the detection and characterization of CA. Analysis of cMRF signal timecourse data may improve tissue characterization as compared to analysis of native T₁ and T₂ alone.

Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer

Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians' judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment.

Synergistic multi-contrast cardiac magnetic resonance image reconstruction

Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.