Technology
Quantitative cardiac T1 mapping via MRI is crucial for diagnosing myocardial diseases such as fibrosis, inflammation, and hypertrophy. Traditional cardiac MRI requires patients to hold their breath, limiting accessibility for pediatric, elderly, and respiratory-compromised individuals. This innovative technology addresses these limitations through an AI-driven, physically-informed deep-learning model. It combines advanced image registration with a quantitative T1 signal relaxation model, correcting motion artifacts from free-breathing MRI scans without the need for extensive training data. The method ensures accurate, motion-robust T1 mapping, enhancing diagnostic reliability and clinical effectiveness.
This self-supervised technology has been rigorously validated, showing superior accuracy (R² = 0.975), anatomical alignment (Dice score = 0.89), and clinical quality compared to existing methods. It represents a significant advancement in cardiac imaging, particularly beneficial for clinically vulnerable populations who struggle with breath-holding during MRI scans.
Advantages
- Enables high-quality cardiac MRI scans under free-breathing conditions.
- Superior motion correction and precise T1 mapping without extensive annotated data.
- Significant improvement in diagnostic accuracy and reproducibility.
- Accessible imaging for pediatric, elderly, and respiratory-compromised patients.
- Potential for seamless integration into MRI systems and cloud-based AI services.
Applications and Opportunities
- Broad applicability across hospitals, imaging centers, and MRI manufacturers.
- Essential for diagnosis and monitoring of myocardial diseases, including fibrosis and inflammation.
- Market potential within the rapidly growing global cardiac MRI segment (projected at $4.2B by 2027).
- Licensing and integration opportunities with leading MRI manufacturers.
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