ArXiv Preprint
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.
François Lecomte, Jean-Louis Dillenseger, Stéphane Cotin
2022-12-15
