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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Deep Learning

    Background:

    • 2D/3D image registration is crucial for image-guided surgery.
    • Conventional methods have limited capture range due to local minima in similarity functions.

    Purpose of the Study:

    • To extend the capture range of 2D/3D registration using a differentiable deep network.
    • To develop a novel Projective Spatial Transformer (ProST) module for improved pose estimation.

    Main Methods:

    • A fully differentiable deep network framework was developed.
    • The network incorporates a Projective Spatial Transformer (ProST) module with unique differentiability for 3D pose parameters.
    • Training utilized a double backward gradient-driven loss function.

    Main Results:

    • The ProST network learned a practical similarity function, significantly extending registration capture range.
    • For pelvis anatomy, ProST followed by CMAES achieved a median TRE of 4.4mm (65.6% SR) in simulation and 2.2mm (73.2% SR) in real data.
    • This represents a substantial improvement over CMAES alone (28.5% and 36.0% SRs).

    Conclusions:

    • The proposed ProST network effectively enhances the capture range of intensity-based 2D/3D registration.
    • The unique differentiability of ProST shows potential for broader applications in 3D medical imaging research.