Transparent Arterial Layer Separation in Cardiac Angiography with Self-Supervised Deep Learning

NU 2023-092

INVENTORS
Sameer Ansari*
Donald Cantrell*
Leon Cho
Syed Hasib Akhter Faruqui
Chaochao Zhou

SHORT DESCRIPTION
This technology enhances cardiac angiography with markedly enhanced visualization of the vasculature by leveraging self-supervised deep neural network-based background subtraction algorithms.

BACKGROUND
Cardiac angiography relies on catheter-based imaging to diagnose vascular disorders with high precision. Current standard-of-care cardiac angiographic images are degraded by superimposed densities from bones and soft tissues, along with cardiac motion.  These imaging limitations reduce vascular clarity and may necessitate repeat acquisitions that increase patient radiation exposure. Despite efforts to enhance vascular clarity and improve background subtraction and registration through various algorithms, existing approaches struggle to balance anatomic vascular fidelity and artifact suppression, underscoring the need for more robust and efficient solutions.

ABSTRACT
This invention proposes an innovative deep neural network-based background subtraction algorithm designed to enhance cardiac angiography imaging. The novel approach integrates multiple deep learning techniques—such as deformable registration, unsupervised vessel layer estimation, and advanced 2D/3D U-Net architectures—to enhance vascular clarity and suppress background density in coronary angiographic image sequences. By generating vessel masks, calculating optical flow, and performing vessel layer separation, the system enables robust subtraction of non-vascular structures. Consequently, this AI-enhanced cardiac angiography overcomes previous imaging obstacles and enables a clearer vasculature visualization capability.

APPLICATIONS

• Clinical Diagnostic Imaging Software Integration
  • Provides superior imaging for coronary angiography.

ADVANTAGES

• State-of-the-Art Vascular Enhancement with Advanced Deep Learning
  • Enables clearer separation of vessel structures.
• Cost and Radiation Exposure Reduction
  • Higher quality scans reduces frequency and cost of repeat scans and limits radiation exposure.
• Translational Technology
  • Adaptable algorithms can be applied to cerebral and peripheral vasculature imaging.

PUBLICATIONS
Sameer Ansari, Donald Cantrell et al, Transparent Arterial Layer Separation in Cardiac Angiography with Self-Supervised Deep Learning, INSTITUTE OF INDUSTRIAL AND SYSTEMS ENGINEERS (IISE) Annual Conference & Expo, May 21, 2023.

IP STATUS
Patent Pending