RadGS-Reg: Registering Spine CT with Biplanar X-rays via Joint 3D Radiative Gaussians Reconstruction and 3D/3D Registration

Kavli Affiliate: Feng Wang

| First 5 Authors: Ao Shen, Ao Shen, , ,

| Summary:

Computed Tomography (CT)/X-ray registration in image-guided navigation
remains challenging because of its stringent requirements for high accuracy and
real-time performance. Traditional "render and compare" methods, relying on
iterative projection and comparison, suffer from spatial information loss and
domain gap. 3D reconstruction from biplanar X-rays supplements spatial and
shape information for 2D/3D registration, but current methods are limited by
dense-view requirements and struggles with noisy X-rays. To address these
limitations, we introduce RadGS-Reg, a novel framework for vertebral-level
CT/X-ray registration through joint 3D Radiative Gaussians (RadGS)
reconstruction and 3D/3D registration. Specifically, our biplanar X-rays
vertebral RadGS reconstruction module explores learning-based RadGS
reconstruction method with a Counterfactual Attention Learning (CAL) mechanism,
focusing on vertebral regions in noisy X-rays. Additionally, a patient-specific
pre-training strategy progressively adapts the RadGS-Reg from simulated to real
data while simultaneously learning vertebral shape prior knowledge. Experiments
on in-house datasets demonstrate the state-of-the-art performance for both
tasks, surpassing existing methods. The code is available at:
https://github.com/shenao1995/RadGS_Reg.

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