SSDiff: A Contrast-Free Virtual LGE Generator for Acute Myocardial Infarction with Joint Segmentation via Diffusion Model | JoVE Visualize

Area of Science:

Cardiovascular Imaging
Artificial Intelligence in Medicine
Medical Image Analysis

Background:

Myocardial infarction (MI) is a leading cause of mortality, with late gadolinium enhancement (LGE) cardiac MRI being the gold standard for assessing myocardial viability.
Current LGE techniques require contrast agents, complex protocols, and incur additional costs, limiting their widespread clinical use.
Existing virtual LGE methods often overlook T2-weighted short-tau inversion recovery (T2-STIR) sequences, crucial for detecting acute MI edema, and typically need manual infarct delineation.

Purpose of the Study:

To develop and validate SSDiff, a multitask conditional diffusion framework for synthesizing contrast-free virtual LGE images from routine cine and T2-STIR cardiac MRI.
To enable simultaneous segmentation of myocardium, ventricular blood pool, and infarct regions using the proposed framework.
To provide a clinically feasible and cost-effective alternative for acute myocardial infarction assessment.

Main Methods:

SSDiff employs a multitask conditional diffusion model integrating cine and T2-STIR sequences for virtual LGE synthesis.
A feature-disentangled attention module isolates sequence-specific information to guide the diffusion process.
A cross-fusion module aligns synthesis and segmentation decoders, enabling mutual optimization for improved accuracy.

Main Results:

SSDiff demonstrated significant improvements in synthetic virtual LGE image quality compared to existing methods.
The framework achieved superior accuracy in segmenting myocardium, blood pool, and infarct regions.
Evaluation on a multi-center, multi-vendor dataset of 409 subjects confirmed the robustness and generalizability of SSDiff.

Conclusions:

SSDiff offers a promising, contrast-free approach for assessing myocardial viability and infarct extent in acute MI.
The generated paired image-mask samples can augment training datasets for AI models, addressing scarcity of LGE-scarred training data.
SSDiff holds significant practical utility and translational potential as a clinically feasible alternative to conventional LGE-MRI.