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C-arm Guidance: A Self-supervised Approach To Automated Positioning During Stroke Thrombectomy

Ahmad Arrabi, Jay hwasung Jung, J Le, A Nguyen, J Reed, E Stahl, Nathan Franssen, Scott Raymond, Safwan Wshah

TL;DR

This work tackles automated C-arm guidance for time-sensitive stroke thrombectomy by introducing a self-supervised regression pretext task that localizes 3D skeletal landmark positions from X-ray images, conditioned on demographic data. A ResNet-34 backbone with cross-attention and a transformer learns anatomy-aware representations, which are then used for landmark classification; a regression head optimizes $L_{reg} = \mathrm{MSE}(P, P)$. The approach yields a mean positional error of about $4.7$ mm and achieves state-of-the-art classification when initialized from the regression pretext, highlighting the value of domain-specific pretraining and demographic information. The study advances toward fully autonomous C-arm trajectory planning by leveraging synthetic DRRs and a GUI for annotation, with plans to extend to rotational/depth cues and clinical fluoroscopy validation to reduce radiation and motion during procedures.

Abstract

Thrombectomy is one of the most effective treatments for ischemic stroke, but it is resource and personnel-intensive. We propose employing deep learning to automate critical aspects of thrombectomy, thereby enhancing efficiency and safety. In this work, we introduce a self-supervised framework that classifies various skeletal landmarks using a regression-based pretext task. Our experiments demonstrate that our model outperforms existing methods in both regression and classification tasks. Notably, our results indicate that the positional pretext task significantly enhances downstream classification performance. Future work will focus on extending this framework toward fully autonomous C-arm control, aiming to optimize trajectories from the pelvis to the head during stroke thrombectomy procedures. All code used is available at https://github.com/AhmadArrabi/C_arm_guidance

C-arm Guidance: A Self-supervised Approach To Automated Positioning During Stroke Thrombectomy

TL;DR

This work tackles automated C-arm guidance for time-sensitive stroke thrombectomy by introducing a self-supervised regression pretext task that localizes 3D skeletal landmark positions from X-ray images, conditioned on demographic data. A ResNet-34 backbone with cross-attention and a transformer learns anatomy-aware representations, which are then used for landmark classification; a regression head optimizes . The approach yields a mean positional error of about mm and achieves state-of-the-art classification when initialized from the regression pretext, highlighting the value of domain-specific pretraining and demographic information. The study advances toward fully autonomous C-arm trajectory planning by leveraging synthetic DRRs and a GUI for annotation, with plans to extend to rotational/depth cues and clinical fluoroscopy validation to reduce radiation and motion during procedures.

Abstract

Thrombectomy is one of the most effective treatments for ischemic stroke, but it is resource and personnel-intensive. We propose employing deep learning to automate critical aspects of thrombectomy, thereby enhancing efficiency and safety. In this work, we introduce a self-supervised framework that classifies various skeletal landmarks using a regression-based pretext task. Our experiments demonstrate that our model outperforms existing methods in both regression and classification tasks. Notably, our results indicate that the positional pretext task significantly enhances downstream classification performance. Future work will focus on extending this framework toward fully autonomous C-arm control, aiming to optimize trajectories from the pelvis to the head during stroke thrombectomy procedures. All code used is available at https://github.com/AhmadArrabi/C_arm_guidance
Paper Structure (7 sections, 1 equation, 3 figures, 4 tables)

This paper contains 7 sections, 1 equation, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Dataset
  3. Methodology
  4. Experiments
  5. Conclusion and Future Works
  6. Compliance with Ethical Standards
  7. Acknowledgments

Figures (3)

  • Figure 1: GUI visualization. The annotators were given direct instructions on collecting the 20 landmarks (top right).
  • Figure 2: Landmarks of spatial locations across the skeleton (left), and their distribution in our annotated dataset (right).
  • Figure 3: Main architecture of our proposed framework. The learned representations in the regression task are used to fine-tune the downstream classification task.