NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiovascular Medicine

Background:

• Cardiovascular diseases (CVDs) are a leading global health concern.
• 2D X-ray invasive coronary angiography (ICA) is standard for CVD assessment but struggles with 3D vessel geometry interpretation.
• Limited projections in ICA restrict 3D coronary tree reconstruction.

Purpose of the Study:

• To develop a self-supervised deep learning method for 3D coronary artery tree reconstruction from limited 2D projections.
• To overcome the limitations of interpreting 3D coronary vessel geometry from 2D invasive coronary angiography.
• To enable accurate 3D reconstruction without requiring 3D ground truth data.

Main Methods:

• Proposed NeCA, a self-supervised deep learning approach utilizing neural implicit representation.
• Employed a multiresolution hash encoder and a differentiable cone-beam forward projector layer.
• Validated the method on a dataset derived from coronary computed tomography angiography (CCTA).

Main Results:

• NeCA achieved promising performance in 3D coronary artery tree reconstruction.
• Demonstrated effective preservation of vessel topology and branch connectivity.
• Outperformed supervised deep learning models without requiring 3D ground truth or large datasets.

Conclusions:

• NeCA offers a novel self-supervised solution for 3D coronary artery reconstruction from limited 2D ICA data.
• The method shows potential for improving 3D visualization in cardiac interventions.
• NeCA's ability to work without extensive training data is a significant advantage.