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DigiWarp: a method for deformable mouse atlas warping to surface topographic data.

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This study introduces a novel method to accurately map mouse anatomy for preclinical imaging. The technique warps a 3D mouse atlas to fit optical surface data, improving image reconstruction accuracy.

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Area of Science:

  • Biomedical Imaging
  • Computational Anatomy
  • Preclinical Research

Background:

  • Accurate quantitative imaging in preclinical optical tomography requires precise estimation of animal surface topography and internal anatomy.
  • Existing methods struggle with non-trivial internal anatomy estimation and challenges in fitting 3D anatomical atlases to variable mouse postures and morphologies, especially with partial data acquisition.

Purpose of the Study:

  • To develop and evaluate a method for fitting a deformable mouse atlas to surface topographic range data obtained from optical systems.
  • To improve the quantitative accuracy of reconstructed images in preclinical bioluminescence or fluorescence optical tomography by accurately estimating internal anatomy.

Main Methods:

  • A deformable mouse atlas is fitted to surface topographic range data using landmark constraints for posture initialization.
  • Registration is achieved by minimizing the asymmetric L(2) pseudo-distance between the atlas and mouse surfaces, with a Laplacian prior ensuring a smooth warping field.
  • Internal anatomy is transformed via elastic energy minimization after the atlas surface is normalized to the range data.

Main Results:

  • Performance evaluation involved measuring volumetric overlap between MRI/CT-delineated organs and those estimated by the proposed warping scheme.
  • High Dice coefficients indicated excellent overlap for the brain and heart.
  • Fair agreement was observed for the kidneys and bladder, demonstrating the method's potential.

Conclusions:

  • The presented method effectively fits a deformable mouse atlas to optical surface data, enabling accurate internal anatomy estimation for preclinical imaging.
  • This approach enhances the quantitative accuracy of reconstructed images in optical tomography.
  • The results show promising accuracy, particularly for brain and heart structures, with potential for broader applications in preclinical research.