# 3D Guidewire Shape Reconstruction from Monoplane Fluoroscopic Images

arXiv: https://arxiv.org/abs/2311.11209 · HTML: https://mlsanity.com/paper/2311.11209/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 19 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Self-Supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images](https://mlsanity.com/paper/2107.04644/) (2021)
- [End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention](https://mlsanity.com/paper/2006.09117/) (2020)
- [Self-Supervised Depth Estimation in Laparoscopic Image using 3D Geometric Consistency](https://mlsanity.com/paper/2208.08407/) (2022)
- [CathSim: An Open-source Simulator for Endovascular Intervention](https://mlsanity.com/paper/2208.01455/) (2022)
- [Observation Space Matters: Benchmark and Optimization Algorithm](https://mlsanity.com/paper/2011.00756/) (2020)

## Built on by (most cited first, 1 of 1)

- [Autonomous Catheterization with Open-source Simulator and Expert Trajectory](https://mlsanity.com/paper/2401.09059/) (2024)

## Abstract (arXiv)

Endovascular navigation, essential for diagnosing and treating endovascular diseases, predominantly hinges on fluoroscopic images due to the constraints in sensory feedback. Current shape reconstruction techniques for endovascular intervention often rely on either a priori information or specialized equipment, potentially subjecting patients to heightened radiation exposure. While deep learning holds potential, it typically demands extensive data. In this paper, we propose a new method to reconstruct the 3D guidewire by utilizing CathSim, a state-of-the-art endovascular simulator, and a 3D Fluoroscopy Guidewire Reconstruction Network (3D-FGRN). Our 3D-FGRN delivers results on par with conventional triangulation from simulated monoplane fluoroscopic images. Our experiments accentuate the efficiency of the proposed network, demonstrating it as a promising alternative to traditional methods.