CNN-based real-time 2D-3D deformable registration from a single X-ray projection

TL;DR

This paper introduces a CNN-based method for real-time 2D-3D deformable registration from a single X-ray, enabling accurate alignment of preoperative 3D scans with intraoperative 2D images for surgical guidance.

Contribution

The paper presents a novel neural network approach that estimates 3D displacement fields from a single 2D X-ray, handling pose uncertainties for clinical applications.

Findings

Achieves mean TRE of 2.3 to 5.5 mm on lung CT data

Handles pose uncertainty effectively in registration

Operates in real-time suitable for surgical use

Abstract

Purpose: The purpose of this paper is to present a method for real-time 2D-3D non-rigid registration using a single fluoroscopic image. Such a method can find applications in surgery, interventional radiology and radiotherapy. By estimating a three-dimensional displacement field from a 2D X-ray image, anatomical structures segmented in the preoperative scan can be projected onto the 2D image, thus providing a mixed reality view. Methods: A dataset composed of displacement fields and 2D projections of the anatomy is generated from the preoperative scan. From this dataset, a neural network is trained to recover the unknown 3D displacement field from a single projection image. Results: Our method is validated on lung 4D CT data at different stages of the lung deformation. The training is performed on a 3D CT using random (non domain-specific) diffeomorphic deformations, to which perturbations mimicking the pose uncertainty are added. The model achieves a mean TRE over a series of landmarks ranging from 2.3 to 5.5 mm depending on the amplitude of deformation. Conclusion: In this paper, a CNN-based method for real-time 2D-3D non-rigid registration is presented. This method is able to cope with pose estimation uncertainties, making it applicable to actual clinical scenarios, such as lung surgery, where the C-arm pose is planned before the intervention.