Segmentation of the Left Atrium in Cardiovascular Magnetic Resonance Images of Patients with Myocarditis

Summary

The three-dimensional fully convolutional network-enhanced cine analysis enables precise left atrial function assessment in myocarditis, improving early systolic-diastolic dysfunction detection and reducing ejection fraction prediction errors for clinical diagnosis and monitoring of atrial mechanical dysfunction.

Abstract

Cardiovascular magnetic resonance (CMR) cine sequences serve as the cornerstone imaging technique for evaluating dynamic left atrial (LA) function in myocarditis patients. By capturing three-dimensional motion characteristics throughout the cardiac cycle with high temporal resolution, this modality provides critical data for analyzing myocardial contractile coordination and wall motion abnormalities. Key technological innovations, such as dynamic modeling and strain-encoded imaging, enable quantitative assessment of early-stage LA systolic-diastolic dysfunction in myocarditis. However, the primary challenges in cine sequence segmentation involve dynamic artifacts and spatiotemporal continuity modeling of thin-walled structures. Traditional threshold-based segmentation methods demonstrate limited consistency in dynamic sequences due to their inability to capture motion patterns. Deep learning approaches utilizing three-dimensional fully convolutional network (3D-FCN) achieved superior accuracy through three strategic enhancements: (1) Spatiotemporal feature fusion: This employed 3D convolutional kernels to simultaneously extract spatial structures and temporal dimensional features, thereby reducing motion blurring effects. (2) Dynamic skip connections: Incorporated within encoder-decoder architectures, these connections strengthened deformation correlation modeling across different cardiac phases through cross-temporal feature propagation. (3) Lightweight design: By utilizing patch-wise processing and depthwise separable convolutions, computational efficiency was optimized for real-time processing of large-scale four-dimensional datasets. The 3D-FCN achieved a Dice coefficient of 0.921 for LA segmentation, representing a 12.3% improvement over conventional methods. This design reduced the LA ejection fraction prediction error from 8.7% to 3.2%. The segmentation results directly facilitated the calculation of quantitative metrics, including LA volume-time curves and strain rates. These metrics supported the clinical diagnosis of myocarditis-associated atrial mechanical dysfunction.

Introduction

Cardiovascular magnetic resonance (CMR) cine sequences, as the core imaging modality for evaluating dynamic left atrial (LA) function in myocarditis patients, provide critical data for identifying abnormalities in myocardial contractile coordination and early diastolic dysfunction, leveraging their high temporal resolution and three-dimensional motion capture capabilities^1,2,3,4,5. Through strain-encoded imaging and dynamic motion modeling techniques, CMR cine sequences enable precise detection of myocardia....

Protocol

The Ethics Committee of Chengdu Medical College determined that this study met the criteria for waiver of informed consent. The research adhered to the principles outlined in the Declaration of Helsinki.

1. Patient selection

1. Use the following inclusion criteria: patients with myocarditis meeting clinical criteria for acute myocarditis, including post-viral symptoms, elevated biomarkers, and electrocardiogram (ECG) abnormalities.
2. Exclude patients with the following: ischemic, valvular, or congenital heart disease; secondary myocardial injury from drugs, chemotherapy or infections; non-diagnostic imaging....

Results

On a CMR dataset comprising 200 myocarditis patients, the proposed framework achieved a Dice coefficient of 0.921 for LA segmentation, representing a 12.3% improvement over baseline models, with a processing speed of 18 frames per second (fps). By employing dynamic kernel distillation to transfer high-level abstract features to low-level features, the HD for LA minimum volume (LAV_min) was optimized from 3.2 mm to 1.7 mm (reduction: 46.9%). For thin-walled LA structures (thickness <2 mm), HD decreased from 4.......

Discussion

This study achieved precise quantification of LA structure and function in myocarditis patients through standardized CMR protocols and dedicated LA scanning parameters, combined with efficient feature extraction and automated rapid segmentation using 3D-FCN. The synergistic interaction between optimized imaging parameters and algorithmic innovations establishes a robust technical foundation for clinical translation.

CMR remains the cornerstone tool for assessing LA structural and functional ab.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
3.0 T MRI scanner	Siemens Healthineers	Skyra 3.0 T	High-resolution clinical/research MRI system with advanced cardiac and body imaging capabilities.
3D Slicer	Open-source community	https://www.slicer.org/	Free, open-source software for medical image analysis (segmentation, registration, 3D visualization). Supported by NIH.
PyTorch	Meta Platforms, Inc.	https://pytorch.org/	Open-source deep learning framework with dynamic computation graphs, widely used for AI research and model deployment. Supports GPU acceleration.