ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition2024
Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering.
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Differentiable Forward and Back-Projector for Rigid Motion Estimation in X-Ray Imaging.IEEE transactions on bio-medical engineering · 2026Article
- 3D path planning for robot-assisted vertebroplasty from arbitrary Bi-plane X-ray via differentiable rendering.Frontiers in robotics and AI · 2026Article
- Deep learning-based optimization for accurate multimodal medical image registration.Scientific reports · 2025Article
- PolyPose: Deformable 2D/3D Registration via Polyrigid Transformations.Advances in neural information processing systems · 2025Article
- Article
- Spatiotemporally constrained 3D reconstruction from biplanar digital subtraction angiography.International journal of computer assisted radiology and surgery · 2025Article
- Improving automatic cerebral 3D-2D CTA-DSA registration.International journal of computer assisted radiology and surgery · 2025Article
- Morphology-Based Non-Rigid Registration of Coronary Computed Tomography and Intravascular Images Through Virtual Catheter Path Optimization.IEEE transactions on medical imaging · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Surgical decisions are informed by aligning rapid portable 2D intraoperative images (e.g. X-rays) to a high-fidelity 3D preoperative reference scan (e.g. CT). However, 2D/3D registration can often fail in practice: conventional optimization methods are prohibitively slow and susceptible to local minima, while neural networks trained on small datasets fail on new patients or require impractical landmark supervision. We present DiffPose, a self-supervised approach that leverages patient-specific simulation and differentiable physics-based rendering to achieve accurate 2D/3D registration without relying on manually labeled data. Preoperatively, a CNN is trained to regress the pose of a randomly oriented synthetic X-ray rendered from the preoperative CT. The CNN then initializes rapid intraoperative test-time optimization that uses the differentiable X-ray renderer to refine the solution. Our work further proposes several geometrically principled methods for sampling camera poses from
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.