{"ID":2881280,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.13304","arxiv_id":"2508.13304","title":"Differentiable Forward and Back-Projector for Rigid Motion Estimation in X-ray Imaging","abstract":"Objective: In this work, we propose a framework for differentiable forward and back-projector that enables scalable, accurate, and memory-efficient gradient computation for rigid motion estimation tasks. Methods: Unlike existing approaches that rely on auto-differentiation or that are restricted to specific projector types, our method is based on a general analytical gradient formulation for forward/backprojection in the continuous domain. A key insight is that the gradients of both forward and back-projection can be expressed directly in terms of the forward and back-projection operations themselves, providing a unified gradient computation scheme across different projector types. Leveraging this analytical formulation, we develop a discretized implementation with an acceleration strategy that balances computational speed and memory usage. Results: Simulation studies illustrate the numerical accuracy and computational efficiency of the proposed algorithm. Experiments demonstrates the effectiveness of this approach for multiple X-ray imaging tasks we conducted. In 2D/3D registration, the proposed method achieves ~8x speedup over an existing differentiable forward projector while maintaining comparable accuracy. In motion-compensated analytical reconstruction and cone-beam CT geometry calibration, the proposed method enhances image sharpness and structural fidelity on real phantom data while showing significant efficiency advantages over existing gradient-free and gradient-based solutions. Conclusion: The proposed differentiable projectors enable effective and efficient gradient-based solutions for X-ray imaging tasks requiring rigid motion estimation.","short_abstract":"Objective: In this work, we propose a framework for differentiable forward and back-projector that enables scalable, accurate, and memory-efficient gradient computation for rigid motion estimation tasks. Methods: Unlike existing approaches that rely on auto-differentiation or that are restricted to specific projector t...","url_abs":"https://arxiv.org/abs/2508.13304","url_pdf":"https://arxiv.org/pdf/2508.13304v1","authors":"[\"Xiao Jiang\",\"Xin Wang\",\"Ali Uneri\",\"Wojciech B. Zbijewski\",\"J. Webster Stayman\"]","published":"2025-08-18T18:44:08Z","proceeding":"physics.med-ph","tasks":"[\"physics.med-ph\",\"eess.IV\"]","methods":"[]","has_code":false}