Summary:
UCLA researchers from the Department of Medicine have developed a method for synthetic microvascular network generation for improved coronary disease monitoring.
Background:
Synthetic microvascular network modeling can be used in diagnosing and managing microvascular diseases such as coronary microvascular disease (CMD). CMD is traditionally diagnosed using invasive techniques that require specialized equipment, limiting their routine use. Noninvasive imaging techniques have emerged as promising alternatives, but existing methods suffer from low and inadequate resolution for precise diagnosis. Computational modeling approaches have been recently developed for this purpose but are constrained by simplified assumptions about vascular structure and blood flow. There remains an unmet need for an advanced method to generate realistic and patient-specific models of microvascular networks for the precise and non-invasive diagnosis of microvascular disorders.
Innovation:
UCLA researchers from the Department of Medicine have developed a novel method to generate patient-specific synthetic microvascular networks for disease diagnosis. The model integrates imaging biomarkers such as myocardial blood volume maps obtained using traditional imaging techniques such as MRI with three-dimensional models of epicardial coronary arteries to generate a synthetic model of the coronary microvasculature with a resolution of 100 μm. This novel method captures the unique features of a patient’s coronary microvasculature, allowing for accurate and non-invasive computation of different diagnostic metrics. This system can enhance diagnostic precision of different diseases and reduce the dependency on invasive procedures by providing a patient specific computational platform.
Potential Applications:
• Coronary microvascular disease diagnosis
• Therapeutic guidance
• Cancer and tumor microenvironment modeling
• Education and training
• Drug development
Advantages:
• Patient specific
• Non-invasive
• High resolution
• Integration with existing imaging
Development-To-Date:
The inventors have successfully demonstrated this innovation and are submitted a journal manuscript for publication.
Reference:
UCLA Case No. 2024-259
Lead Inventor:
Kim-Lien Nguyen