We conducted a comprehensive search to identify studies on the use of CMR in myocarditis across multiple databases, including PubMed, Embase, Medline, Web of Science (WOS), and Cochrane, covering publications from 2017 to 2025. The search covered publications from January 2017 to March 2025 to capture the most recent advancements, particularly in low-field MRI. The search strategy incorporated various keyword combinations related to MRI modalities (“MRI,” “magnetic resonance imaging”), cardiac conditions (“cardiac,” “heart,” “cardiovascular,” “myocarditis”), and applications (“utility,” “feasibility,” “diagnostic value,” “technical challenges,” “clinical use”). A specific focus was given to low-field, portable, and point-of-care MRI, particularly in emergency settings (“emergency,” “urgent,” “ED,” “ER”). Boolean operators (AND, OR) and field-specific filters (e.g., Title/Abstract) were used to refine results.

Inclusion criteria encompassed original research articles, clinical trials, and case reports involving human subjects, published in English, that investigated CMR for myocarditis diagnosis, risk stratification, or outcome prediction. Reviews, editorials, and non-English publications were excluded. The initial search yielded a broad set of articles, which were screened by title and abstract for relevance. The full text of potentially eligible articles was then reviewed to confirm their alignment with the review's focus, particularly on emergency and point-of-care applications. Data on study design, patient population, CMR protocols, diagnostic performance, and outcomes were extracted from the included studies.

Our review of the publications about CMR in cardiac diseases from 2017 to 2025 reveals that myocarditis is the third most common pathology studied in research among any CMR diagnoses, after coronary disease and cardiomyopathy (Figure 1).

This trend highlights the growing importance of CMR in cardiac imaging research, particularly in the use of CMR for myocarditis management. Among these studies, CMR is widely used for the diagnosis, monitoring, and prognosis of myocarditis (Figure 2).

Although CMR has been utilized in different stages of myocarditis, only two studies were conducted in the ED setting (24, 25). However, the second study conducted by Alotaibi et al. (25), focused on recurrent myocarditis presented to the ED. This indicates that, despite CMR's excellent diagnostic yield and its paramount importance in the accurate diagnosis of myocarditis, its utility in the ED is not well established.

The clinical and imaging features of myocarditis often overlap with other cardiac conditions, such as myocardial infarction (MI), Takotsubo cardiomyopathy, and sarcoidosis (26), making accurate differentiation challenging, especially in patients with elevated troponin levels and nonspecific ECG changes (27). While coronary angiography can rule out obstructive coronary disease, it does not assess myocardial inflammation, underscoring the need for CMR in these cases (15).

Patients with Takotsubo cardiomyopathy frequently present with acute chest pain, dyspnea, ECG changes (including ST-segment elevation or T-wave inversion), and elevated cardiac troponin—features that are clinically indistinguishable from myocarditis or ACS at presentation. Importantly, Takotsubo cardiomyopathy is often triggered by emotional or physical stress and predominantly affects postmenopausal women, but these epidemiologic clues are not sufficiently specific to reliably guide early diagnosis in the ED (28).

Conventional diagnostic tools further contribute to this overlap. Echocardiography may demonstrate regional wall motion abnormalities in both Takotsubo cardiomyopathy and myocarditis, while coronary angiography may be normal in both conditions, particularly in myocarditis and Takotsubo syndrome without obstructive coronary disease. As a result, relying solely on symptoms, biomarkers, ECG findings, and initial imaging may lead to misclassification and delayed or inappropriate management. Cardiac magnetic resonance imaging plays a critical role in resolving this diagnostic ambiguity. CMR can reliably differentiate Takotsubo cardiomyopathy from myocarditis (15, 26).

Atypical acute myocarditis, presenting with chest pain but normal ECG and troponin levels, is frequently misdiagnosed as acute coronary syndrome (ACS), leading to delays in treatment (29). Conventional imaging modalities such as echocardiography, cardiac CT, and point-of-care echocardiography (POCUS) lack the sensitivity and specificity required for a definitive diagnosis (30, 31). Characteristic subepicardial and mid-wall late gadolinium enhancement (LGE) patterns are highly specific for myocarditis and correlate with disease severity and prognosis (32). Furthermore, CMR has been instrumental in identifying myocardial inflammation in post-Coronavirus Disease-2019 (COVID-19) and vaccine-associated myocarditis, highlighting its expanding role in clinical practice (33).

With refined imaging protocols and diagnostic criteria, CMR is increasingly recognized as the gold standard for noninvasive myocarditis diagnosis, offering superior accuracy compared to conventional imaging and even endomyocardial biopsy (34). As demonstrated for other causes of chest pain, exploring the integration of CMR into emergency settings could, pending further validation, significantly enhance early diagnosis and management, potentially reducing misdiagnosis and invasive procedures (35).

The Lake Louise Criteria, first introduced in 2009, revolutionized CMR myocarditis diagnosis by incorporating T2-weighted imaging (for edema), early T1-based LGE sequences (for hyperemia), and late LGE sequences (for necrosis and fibrosis) (36). The 2018 mLLC established a systematic approach to diagnosing myocarditis using CMR by detecting myocardial inflammation through both T1- and T2-based techniques (37) (Figure 3). The T2-based criterion identifies myocardial edema, a marker of acute inflammation, which can be assessed using traditional T2-weighted STIR sequences or the more sensitive quantitative T2 mapping (38). The T1-based criterion confirms additional myocardial abnormalities through LGE in a nonischemic pattern, such as subepicardial or mid-myocardial enhancement, or through quantitative techniques like native T1 mapping and ECV quantification, which allow for precise tissue characterization (38). According to the mLLC, the presence of at least one T2 abnormality and one T1 abnormality is required for a definitive diagnosis of myocarditis (37–39).

Quantitative mapping techniques have significantly improved the accuracy of myocarditis detection by enabling quantitative assessments of myocardial tissue properties, particularly in identifying early and borderline cases of myocarditis (40). Table 1 summarizes the key parametric mapping techniques, T1/T2 mapping and post-contrast T1 mapping-derived ECV quantification, with their unique properties in the detection and evaluation of myocarditis.

The inclusion of novel parametric mapping techniques, native T1 mapping, T2 mapping, and ECV in the 2018 mLLC has significantly enhanced diagnostic yield compared to the original criteria. In a validation study, the mLLC achieved a sensitivity of 87.5% (95% CI: 73.9%, 94.5%) and a specificity of 96.2% (95% CI: 81.1%, 99.3%), outperforming the original LLC criteria, which had a sensitivity of 72.5% (95% CI: 57.2%, 83.9%) (41). It is important to note that these performance metrics are derived from specific cohorts and may vary in different clinical settings. Several studies have further explored the combination of these parametric mapping techniques, resulting in improved sensitivity and specificity, with Li et al. reporting a high diagnostic performance when combining native T1 and T2 mapping, showing a diagnostic accuracy of 92% for myocarditis detection (42). When used together, parametric mapping techniques provide a comprehensive, noninvasive approach to diagnosing myocarditis, improving both diagnostic accuracy and the ability to monitor disease evolution over time (37).

Unlike high-field MRI systems, low-field MRI provides a more accessible alternative in various clinical settings. Recent studies have demonstrated the feasibility of using portable low-field MRI for point-of-care neuroimaging, offering actionable results within minutes. This capability may significantly improve patient outcomes in time-sensitive scenarios such as traumatic brain injury, musculoskeletal imaging, and pulmonary pathologies (61–63).

Studies such as those by Littlewood et al. and Lin et al. demonstrated the capability of low-field MRIs to perform advanced imaging techniques with acceptable diagnostic accuracy in motion-corrected, 3D LGE imaging and free-breathing scans (19, 64). Furthermore, low-field MRI systems can be used in rural or remote EDs, as seen in Lin et al.'s report of a portable 0.5T MRI scanner for diagnosing myocarditis in underserved regions (54).

Recent pilot studies demonstrate the initial feasibility of low-field CMR in EDs, addressing gaps in clinical validation. For example, Littlewood et al. successfully implemented a 0.55T MRI system in an ED workflow, performing motion-corrected 3D LGE imaging in hemodynamically stable patients with suspected myocarditis (19). Their protocol achieved diagnostic accuracy comparable to that of 1.5T systems, with 88% concordance in detecting subepicardial fibrosis and edema, while reducing scan times to 25–35 min (19). Similarly, Lin et al. validated free-breathing, AI-reconstructed low-field CMR (0.35T) in rural EDs, reporting 92% agreement with high-field CMR for diagnosing acute myocarditis in patients with nondiagnostic echocardiography (64). This study highlighted low-field CMR's utility in triaging high-risk patients (e.g., troponin-negative chest pain) while minimizing claustrophobia-related scan failures (<5% vs. 15% in 1.5T systems) (64). As shown in Table 2, key parametric measurements such as ECV quantification and T1 mapping in selected studies exhibit correlations between low-field (0.35–0.55T) and high-field (1.5–3T) systems, but these findings are vendor-, sequence-, and site-specific (65). For instance, Lin et al. used free-breathing T2 mapping, whereas Littlewood et al. prioritized abbreviated LGE protocols (19, 64). Standardization efforts, such as the MYOFLAME-19 trial (43), aim to establish consensus workflows for low-field CMR in acute care.

Both the 2025 ESC Guidelines and the 2024 ACC Expert Consensus emphasize cardiac magnetic resonance imaging as the cornerstone of non-invasive myocarditis diagnosis, particularly when the clinical presentation is atypical or when biomarkers are inconclusive. The ACC pathway specifically highlights the emergency department as a key decision point, recommending early CMR in patients with chest pain, elevated troponin, and non-obstructive coronary arteries (17, 18).

One of the major advantages of low-field MRI is its reduced magnetic field strength, which mitigates safety concerns, especially for patients with ferromagnetic implants. With lower magnetic forces, risks like tissue heating, device displacement, or malfunction are minimized (56). Additionally, low-field MRI systems produce fewer susceptibility artifacts compared to high-field MRI, enhancing diagnostic accuracy. A study also highlighted the quieter operation of low-field MRI systems, which improves patient comfort, especially for those sensitive to noise or has anxiety during imaging procedures. 84% of patients rated the noise levels of 0.55T MRI as “better” or “much better” than a 1.5T MRI (55). These patient safety parameters are of utmost importance in ED patients who are acutely symptomatic and anxious while receiving urgent care.

Many low-field MRI systems come with open configurations, which can be more comfortable for patients with claustrophobia or obesity. However, some studies reported a higher incidence of claustrophobia in open MRIs as well, which could be attributable to prior negative MRI experiences (58).

AI plays a significant role in overcoming the challenge of low signal-to-noise ratio (SNR) in low-field MRI systems, thus improving image quality. AI-driven models such as LoHiResGAN (Low- to High-Resolution Generative Adversarial Network) have shown significant improvements in image quality, especially in brain MRI (66). Moreover, AI models like CNN and U-net have been used to enhance scan timing, image reconstruction, resolution, myocardial segmentation, quantification, and T1/T2 mapping. These tools are not yet standardized or widely approved for clinical use.

Several studies have confirmed the feasibility of advanced imaging techniques in low-field MRIs in research contexts. Kaushal et al. reported equivalent and reliable diagnostic correlations between ECV measurements at 0.55 and 1.5T, while Varghese et al. demonstrated the feasibility of advanced imaging techniques such as phase-contrast imaging and T1/T2 mapping at 0.35T (67). Additionally, Doerner et al. highlighted the potential of strain analysis at low-field strengths for detecting minor myocardial changes, adding diagnostic value in cases like myocarditis, where clinical symptoms can be nonspecific (68).

Low-field MRI has the potential to reduce economic costs significantly, making it an attractive option for emergency and resource-limited settings. Estimated purchase prices are often cited in the range of $150,000–$500,000, which is 70%–80% lower than high-field systems (often >$1 million, up to $3 million for premium 3T systems), offering substantial upfront savings. Installation costs may also be reduced by eliminating the need for heavily shielded rooms, potentially saving $200,000–$500,000 in infrastructure upgrades (69). Operationally, low-field CMR may lower annual maintenance costs ($20,000 vs. $100,000–$150,000 for high-field) by eliminating the need for heavily shielded rooms, potentially saving $200,000–$500,000 in infrastructure upgrades (19). Its portability could enable deployment in standard ED bays without facility renovations (22). A detailed micro-level cost-effectiveness analysis (CEA) based on device utilization, scan time, personnel, and patient outcomes would be further needed to confirm the potential savings.

Despite the potential advantages, low-field CMR still faces challenges such as low spatial resolution and low SNR, which may hinder the ability to distinguish between different tissue types. For instance, myocardial fibrosis and subtle edema may be difficult to diagnose due to these limitations. Recent innovations, such as the optimization of pulse sequences for low-field systems and advanced motion correction algorithms, are actively being developed to overcome these limitations (59). Additionally, techniques like compressed sensing, parallel imaging, and machine learning-based approaches are being shown in research settings to improve SNR and spatial resolution, making low-field CMR more suitable for clinical use (66, 69).

Standardized imaging protocols are crucial for enhancing the diagnostic accuracy and reproducibility of low-field CMR in myocarditis. A recent study using AI-driven approaches to fine-tune T2 mapping sequences highlights this need (70). AI-driven tools, such as motion correction and noise reduction algorithms, have the potential to improve image quality and ensure consistency across clinical applications, but they require standardization and validation. To guide future research and development for low-field CMR in emergency and outpatient settings, we propose a structured imaging workflow that integrates examples of AI-enhanced techniques. The following table (Table 3) outlines a proposed standardized low-field CMR protocol for research applications in the evaluation of suspected myocarditis:

This protocol leverages AI for noise reduction (LoHiResGAN), motion correction (U-net), and rapid analysis, reducing total scan time to 35–45 min while maintaining diagnostic accuracy. For example, Littlewood et al. achieved 88% concordance with high-field CMR using a similar 28-minute protocol (19). Future efforts, such as the MYOFLAME-19 trial (66), aim to validate these workflows against endomyocardial biopsy and clinical outcomes, bridging the standardization gap in low-field CMR.

Advanced biomarkers, such as strain analysis, T1/T2 mapping, and ECV quantification, are critical to expanding the clinical usefulness of low-field CMR. Studies have shown that low-field MRI systems can produce reliable results for assessing myocardial fibrosis and edema, with strong correlations observed between ECV measurements at 0.55 and 1.5T (71). AI-driven improvements in strain analysis, ECV quantification, and image reconstruction can further enhance the diagnostic sensitivity of low-field CMR.

The integration of AI tools into clinical workflows requires careful consideration of regulatory compliance (e.g., FDA, CE marking), reproducibility across different hospitals and patient populations, and robustness against artifacts. AI-based reconstruction and analysis tools are currently in the research stage and should be clearly distinguished as exploratory supports, not the primary basis for clinical decision-making. Prospective comparison with standard methods and external validation are essential before clinical adoption.

To maximize the clinical impact of low-field CMR in emergency care and translate its potential into clinical reality, a structured research agenda and implementation pathway are essential. We propose a multi-faceted approach beginning with ED Prospective Multicenter Trials to compare the diagnostic accuracy, time-efficiency, safety, and cost-effectiveness of abbreviated low-field CMR protocols against standard care (e.g., echocardiography ± CT) for suspected acute myocarditis. Pilot studies suggest such an approach could reduce unnecessary admissions by 30%–40% by providing rapid, definitive diagnosis through motion-corrected T1/T2 mapping and LGE imaging (19, 54).

Concurrently, systematic Image Quality and Artifact Evaluation in diverse patient subgroups (e.g., those with arrhythmia, obesity, or implants) are needed, alongside efforts to validate the diagnostic non-inferiority of Non-Contrast Protocols (such as Virtual Native Enhancement) compared to the standard LGE-based mLLC. To ensure reliability, the development of low-field-specific Quality Control and Assurance protocols, including phantom testing and drift monitoring for quantitative mapping is a critical priority. Finally, this research must culminate in the Standardization and Workflow Integration of consensus protocols for acute care. To guide this future implementation, we propose a structured triage pathway (Figure 4) that integrates rapid, protocol-driven low-field CMR into the ED evaluation of suspected myocarditis following inconclusive initial testing. This algorithm prioritizes patient safety through a mandatory pre-scan checklist assessing hemodynamic stability, renal function (e.g., eGFR >30 mL/min/1.73 m² for contrast), pregnancy status, device compatibility, and procedure tolerance. Within this workflow, AI-based tools for reconstruction and analysis are positioned as exploratory supports for research purposes only, with final clinical decisions resting on standard, validated image interpretation until regulatory approval and broader clinical validation are achieved.