PolyPose: Localizing Deformable Anatomy in 3D from Sparse 2D X-ray Images using Polyrigid Transforms

• URL: http://arxiv.org/abs/2505.19256v3
• Date: Wed, 30 Jul 2025 01:35:16 GMT
• Title: PolyPose: Localizing Deformable Anatomy in 3D from Sparse 2D X-ray Images using Polyrigid Transforms
• Authors: Vivek Gopalakrishnan, Neel Dey, Polina Golland,
• Abstract summary: We present PolyPose, a simple and robust method for deformable 2D/3D registration.<n>PolyPose parameterizes complex 3D deformation fields as a composition of rigid transforms.<n>We show that this strong inductive bias enables PolyPose to successfully align the patient's preoperative volume to as few as two X-ray images.
• Score: 5.617649111108429
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Determining the 3D pose of a patient from a limited set of 2D X-ray images is a critical task in interventional settings. While preoperative volumetric imaging (e.g., CT and MRI) provides precise 3D localization and visualization of anatomical targets, these modalities cannot be acquired during procedures, where fast 2D imaging (X-ray) is used instead. To integrate volumetric guidance into intraoperative procedures, we present PolyPose, a simple and robust method for deformable 2D/3D registration. PolyPose parameterizes complex 3D deformation fields as a composition of rigid transforms, leveraging the biological constraint that individual bones do not bend in typical motion. Unlike existing methods that either assume no inter-joint movement or fail outright in this under-determined setting, our polyrigid formulation enforces anatomically plausible priors that respect the piecewise rigid nature of human movement. This approach eliminates the need for expensive deformation regularizers that require patient- and procedure-specific hyperparameter optimization. Across extensive experiments on diverse datasets from orthopedic surgery and radiotherapy, we show that this strong inductive bias enables PolyPose to successfully align the patient's preoperative volume to as few as two X-ray images, thereby providing crucial 3D guidance in challenging sparse-view and limited-angle settings where current registration methods fail.

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