# Improving automatic cerebral 3D-2D CTA-DSA registration

## TL;DR

This paper introduces DeepIterReg, an AI method that improves 3D to 2D image alignment for stroke interventions, helping doctors during procedures.

## Contribution

A novel AI-driven pipeline for 3D CTA to 2D DSA registration using neural networks and iterative optimization.

## Key findings

- DeepIterReg accurately registered 70% of a test set of 20 patients.
- The method improved capture ranges with initial pose estimation using a convolutional neural network.
- It shows potential to reduce manual adjustments during endovascular stroke interventions.

## Abstract

Stroke remains a leading cause of morbidity and mortality worldwide, despite advances in treatment modalities. Endovascular thrombectomy (EVT), a revolutionary intervention for ischemic stroke, is limited by its reliance on 2D fluoroscopic imaging, which lacks depth and comprehensive vascular detail. We propose a novel AI-driven pipeline for 3D CTA to 2D DSA cross-modality registration, termed DeepIterReg.

The proposed pipeline integrates neural network-based initialization with iterative optimization to align pre-intervention and peri-intervention data. Our approach addresses the challenges of cross-modality alignment, particularly in scenarios involving limited shared vascular structures, by leveraging synthetic data, vein-centric anchoring, and differentiable rendering techniques.

We assess the efficacy of DeepIterReg through quantitative analysis of capture ranges and registration accuracy. Results show that our method can accurately register 70% of a test set of 20 patients and can improve capture ranges when performing an initial pose estimation using a convolutional neural network.

DeepIterReg demonstrates promising performance for 3D-to-2D stroke intervention image registration, potentially aiding clinicians by improving spatial understanding during EVT and reducing dependence on manual adjustments.