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A momentum-based diffeomorphic demons framework for deformable MR-CT image registration.

R Han1, T De Silva1, M Ketcha1

  • 1Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.

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This study introduces pMI-Demons, a novel multi-modality deformable registration method that improves accuracy and speed for neurosurgery guidance. It ensures accurate image alignment, crucial for complex deep brain procedures.

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Area of Science:

  • Medical Imaging
  • Neurosurgery
  • Image Registration

Background:

  • Neuro-navigated procedures demand high geometric accuracy.
  • Deep brain regions are susceptible to deformation errors (e.g., cerebrospinal fluid egress) during neurosurgery.
  • Accurate image registration is essential for precise surgical guidance.

Purpose of the Study:

  • To develop and evaluate a multi-modality, diffeomorphic, deformable registration method for neurosurgery.
  • To improve geometric accuracy in neuro-navigation by addressing complex brain deformations.
  • To enable high-precision image guidance by accurately registering preoperative MRI and intraoperative CT.

Main Methods:

  • Developed pMI-Demons: a multi-modality Demons algorithm extension using pointwise mutual information (pMI).
  • Incorporated momentum-based acceleration for faster convergence.
  • Evaluated performance using phantom studies and clinical data from four minimally invasive neurosurgery patients, comparing with MI-FFD and MI-SyN.

Main Results:

  • pMI-Demons achieved comparable registration accuracy to reference methods (MI-FFD, MI-SyN) with significantly fewer outliers.
  • pMI-Demons and MI-SyN produced diffeomorphic transformations preserving topology, unlike MI-FFD which caused unrealistic deformations.
  • Momentum-based acceleration reduced pMI-Demons runtime by ~35% (10.5 min CPU, 2.2 min GPU), outperforming MI-FFD and MI-SyN.

Conclusions:

  • pMI-Demons offers a robust solution for accurate multi-modality image registration in neurosurgery.
  • The method ensures diffeomorphic transformations, crucial for maintaining anatomical integrity during image-guided procedures.
  • Accelerated runtime facilitates clinical translation for enhanced image-guided neurosurgery.