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Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology.

Xiaofeng Liu1, Fangxu Xing1, Chao Yang2

  • 1Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.

Brainlesion : Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Brainles (Workshop)
|May 20, 2021
PubMed
Summary

This study introduces a novel image inpainting method to improve brain tumor registration. The technique enhances alignment accuracy between patient and healthy brain MRIs for better pathological analysis.

Keywords:
Brain TumorContextual LearningDeep LearningImage InpaintingIrregular StructureRegistrationSymmetry

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Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Artificial Intelligence

Background:

  • Deformable registration of brain MRIs is crucial for tumor geometry specification and pathological analysis.
  • Tumor regions and irregular lesions complicate registration due to mismatched tissue and distorted structures.
  • Accurate registration requires robust methods to handle anatomical variations and missing data.

Purpose of the Study:

  • To develop an advanced image inpainting framework for accurate deformable registration of brain tumor images to healthy controls.
  • To improve the similarity measure and structural understanding in brain MRI registration involving tumors.
  • To enhance pathological analysis through precise alignment of patient-specific tumor data with normal brain atlases.

Main Methods:

  • A multi-step context-aware image inpainting framework generating synthetic tissue intensities within tumor regions.
  • Coarse image-to-image translation for initial inference of missing data, followed by feature-level patch-match refinement.
  • Incorporation of a symmetry constraint to leverage inherent brain anatomical symmetry for improved structure understanding.

Main Results:

  • The proposed inpainting method significantly improved registration accuracy compared to existing techniques.
  • Quantitative metrics showed increased peak signal-to-noise ratio, structural similarity index, and inception score.
  • Reduced L1 error was observed, demonstrating enhanced fidelity in the inpainted tumor regions.
  • Successful patient-to-normal brain image registration was achieved, validating the method's effectiveness.

Conclusions:

  • The developed context-aware inpainting framework effectively addresses challenges in registering brain tumor images.
  • The method provides a robust solution for generating realistic synthetic tissue intensities, crucial for accurate registration.
  • This approach facilitates more precise pathological analysis and anatomical studies of brain tumors.