Physics-Informed Endovasculature Deformation Estimation And Registration

summary

I am building the computer vision subsystem for an endovascular robotic telesurgery platform at CERLAB under Dr. Kenji Shimada and doctoral candidate Rishi Basdeo, with collaborators at the University of Pittsburgh and UPMC. The goal is to estimate intra-operative vessel deformation from fluoroscopy and register that motion back to pre-operative vascular anatomy.

problem

During endovascular procedures, a guidewire is navigated through blood vessels under live X-ray. As the wire advances, it deforms the vessel wall. Predicting that deformation before it becomes unsafe requires recovering 3D vessel displacement from a 2D fluoroscopic projection, which is a severely ill-posed inverse problem.

approach

Since ground-truth intra-operative 3D vessel deformation data does not exist at scale, I generate it synthetically using physics simulation.

Synthetic centerline deformations generated via free-form deformation (FFD). Each frame captures a unique vessel state driven by guidewire interaction physics.

Each simulated frame yields:

Sparse 3D displacement fields at 20 control points along the centerline
Multi-view synthetic X-rays across clinically relevant projections such as AP, Lateral, LAO45, RAO45, Spider, and RAO30_Caudal20
Physics metrics including maximum displacement, mesh strain ratio, and vessel straightening percentage

I built an auditing tool to validate the physics integrity of the generated dataset, verifying that mesh strain stays within the clinical safety threshold of 1.15 and that the simulated deformations remain physically meaningful.

The learning model is MorphPINN, a multimodal network that fuses 2D X-ray image features with 3D geometric context to predict full 3D vessel deformation from a single fluoroscopy frame.

The architecture has three branches:

Vision backbone: a ResNet-18 modified for single-channel fluoroscopy
Geometry and camera encoder: an MLP over rest-state centerline points and projection geometry
Fusion predictor: a deformation regressor over the combined image and geometric features

Training uses a composite loss that enforces both accuracy and physical plausibility:

\[\mathcal{L} = w_{\text{mse}} \cdot \mathcal{L}_{\text{MSE}} + w_{\text{smooth}} \cdot \mathcal{L}_{\text{smooth}} + w_{\text{proj}} \cdot \mathcal{L}_{\text{proj}}\]

Term	Weight	Purpose
\(\mathcal{L}_{\text{MSE}}\)	1.0	Penalizes deviation from ground-truth displacement
\(\mathcal{L}_{\text{smooth}}\)	0.1	Enforces deformation smoothness along the centerline
\(\mathcal{L}_{\text{proj}}\)	1e-5	Penalizes geometric inconsistency with the X-ray projection

A hard strain cap of 1.15 is embedded in the loss, directly encoding the clinical safety constraint that prevents predictions from implying unsafe force thresholds on the vessel wall.

current result

The current system already covers the full engineering loop: synthetic deformation generation, multi-view X-ray rendering, dataset auditing, multimodal model training, and a literature review on robotic endovascular vision systems with the vision subsection written for a journal submission currently under review at Journal of Intelligent and Robotic Systems.

limitations

No large-scale real intra-operative ground-truth deformation dataset exists yet
The current setup focuses on centerline motion rather than full vessel wall mechanics
Blood flow and force thresholds are not yet coupled to the deformation estimate

next steps

Physics-informed synthetic data generation (FFD + guidewire kinematics)
Multi-view X-ray rendering pipeline (6 clinical projections via gVXR)
Dataset physics auditing and validation
MorphPINN architecture and training pipeline
Comprehensive literature review (under review, JIRS)
Synthetic contrast injection to reduce chemical contrast agent dose
Blood flow fluid dynamics integration for force threshold estimation
Full intra-operative vessel motion prediction from guidewire state