Diagnosing acute myocarditis in the emergency department—advancing cardiac MRI with a focus on low-field MR applications

TL;DR

This paper explores how low-field MRI can improve diagnosing acute myocarditis in emergency departments by using AI to enhance image quality and enable faster, safer scans.

Contribution

The paper introduces the use of low-field MRI (<1.5T) with AI-driven reconstruction for diagnosing acute myocarditis in emergency settings.

Findings

Low-field MRI at 0.55T can detect subclinical myocarditis with ECV measurements strongly correlating with 1.5T MRI (r = 0.91).

AI techniques like LoHiResGAN and U-net improve image quality in low-field MRI, enabling cine imaging and strain analysis.

Low-field MRI offers advantages in portability, safety, and cost, making it suitable for emergency departments and resource-limited settings.

Abstract

Acute myocarditis is an inflammatory condition of the myocardium, often triggered by viral infections, autoimmune diseases, or toxins. It can lead to arrhythmias, heart failure, and sudden cardiac death. Early and accurate diagnosis is crucial for timely management and preventing complications. It poses a significant diagnostic challenge in emergency departments (EDs) due to nonspecific symptoms, overlapping features with conditions like acute coronary syndrome, and limitations of conventional diagnostics. Cardiac magnetic resonance imaging (CMR) is the gold standard for noninvasive diagnosis, using the 2018 Modified Lake Louise Criteria (mLLC). However, high-field CMR (1.5–3T) faces barriers in EDs, such as longer scan times, higher cost, lack of accessibility, and contraindications in patients with implantable devices, severe kidney disease, or hemodynamic instability. Low-field MRI (<1.5T) offers advantages in portability, safety, and cost while reducing susceptibility artifacts. Recent advances in AI-driven image reconstruction (e.g., LoHiResGAN, U-net) address low signal-to-noise ratios, enabling cine imaging, strain analysis, and parametric mapping at 0.55T. Studies show that low-field CMR can detect subclinical myocarditis and predict outcomes, with ECV measurements at 0.55T strongly correlating with 1.5T (r = 0.91), demonstrating comparable reliability. By integrating low-field CMR into ED protocols, clinicians can improve early detection of occult myocarditis, guide risk stratification, and reduce long-term morbidity and healthcare costs. Standardization of imaging workflows and AI-enhanced protocols will further bridge diagnostic gaps, particularly in resource-limited settings. This review highlights low-field CMR's potential to redefine acute myocarditis management, balancing diagnostic precision with practicality in emergency care.

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