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Forward and Back Projectors for Gradient-based Rigid Motion Estimation in X-ray Imaging.

Xiao Jiang1, Wojciech B Zbijewski1, J Webster Stayman1

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This study introduces a novel framework for differentiable X-ray projection, enabling efficient gradient computation for motion estimation. This accelerates tasks like 2D/3D registration and motion-compensated reconstruction.

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Reconstruction

Background:

  • Accurate rigid motion estimation is vital for X-ray imaging tasks like 2D/3D registration and motion-compensated reconstruction.
  • Gradient-based optimization is hindered by the lack of efficient, generalizable, and differentiable projectors for motion estimation.

Purpose of the Study:

  • To develop a scalable, accurate, and memory-efficient framework for differentiable forward- and back-projectors.
  • To enable efficient gradient computation for rigid motion estimation in X-ray imaging.

Main Methods:

  • Derived a general analytical gradient formulation for forward and backprojection in the continuous domain.
  • Developed a discretized version with an acceleration strategy for computational efficiency and memory management.
  • Utilized the insight that motion gradients can be expressed using the original projection operators.

Main Results:

  • Achieved an approximately 8x speedup in 2D/3D registration compared to existing methods with similar accuracy.
  • Enhanced image sharpness and structural fidelity in motion-compensated analytical reconstruction on phantom data.
  • Demonstrated substantial efficiency gains over existing gradient-based methods.

Conclusions:

  • The proposed framework offers a unified and efficient approach to gradient computation for differentiable X-ray projection.
  • This method significantly improves performance and efficiency in various X-ray imaging applications requiring accurate motion estimation.