Robust and Accurate Multi-view 2D/3D Image Registration with Differentiable X-ray Rendering and Dual Cross-view Constraints
This addresses the challenge of aligning preoperative models with intraoperative images in medical procedures, offering an incremental improvement over existing methods.
The paper tackled the problem of robust and accurate multi-view 2D/3D image registration for interventional navigation by proposing a two-stage approach with differentiable X-ray rendering and dual cross-view constraints, achieving a mean target registration error of 0.79 ± 2.17 mm on the DeepFluoro dataset.
Robust and accurate 2D/3D registration, which aligns preoperative models with intraoperative images of the same anatomy, is crucial for successful interventional navigation. To mitigate the challenge of a limited field of view in single-image intraoperative scenarios, multi-view 2D/3D registration is required by leveraging multiple intraoperative images. In this paper, we propose a novel multi-view 2D/3D rigid registration approach comprising two stages. In the first stage, a combined loss function is designed, incorporating both the differences between predicted and ground-truth poses and the dissimilarities (e.g., normalized cross-correlation) between simulated and observed intraoperative images. More importantly, additional cross-view training loss terms are introduced for both pose and image losses to explicitly enforce cross-view constraints. In the second stage, test-time optimization is performed to refine the estimated poses from the coarse stage. Our method exploits the mutual constraints of multi-view projection poses to enhance the robustness of the registration process. The proposed framework achieves a mean target registration error (mTRE) of $0.79 \pm 2.17$ mm on six specimens from the DeepFluoro dataset, demonstrating superior performance compared to state-of-the-art registration algorithms.