MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction
This addresses the challenge of detailed anatomical reconstruction for digital twin cardiac analysis, representing a significant advancement but is incremental as it builds on existing methods with specific improvements.
The study tackled the problem of inaccurate cardiac model reconstruction from anisotropic Cardiac Magnetic Resonance (CMR) images by introducing MorphiNet, a network that uses unpaired high-resolution CT images to learn heart anatomy, resulting in approximately 40% higher Dice scores, half the Hausdorff distance, and around 3 mm average surface error compared to state-of-the-art methods.
Cardiac Magnetic Resonance (CMR) imaging is widely used for heart modelling and digital twin computational analysis due to its ability to visualize soft tissues and capture dynamic functions. However, the anisotropic nature of CMR images, characterized by large inter-slice distances and misalignments from cardiac motion, poses significant challenges to accurate model reconstruction. These limitations result in data loss and measurement inaccuracies, hindering the capture of detailed anatomical structures. This study introduces MorphiNet, a novel network that enhances heart model reconstruction by leveraging high-resolution Computer Tomography (CT) images, unpaired with CMR images, to learn heart anatomy. MorphiNet encodes anatomical structures as gradient fields, transforming template meshes into patient-specific geometries. A multi-layer graph subdivision network refines these geometries while maintaining dense point correspondence. The proposed method achieves high anatomy fidelity, demonstrating approximately 40% higher Dice scores, half the Hausdorff distance, and around 3 mm average surface error compared to state-of-the-art methods. MorphiNet delivers superior results with greater inference efficiency. This approach represents a significant advancement in addressing the challenges of CMR-based heart model reconstruction, potentially improving digital twin computational analyses of cardiac structure and functions.