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LEARNING ACCURATE RIGID REGISTRATION FOR LONGITUDINAL BRAIN MRI FROM SYNTHETIC DATA.

Jingru Fu1, Adrian V Dalca2,3,4,5, Bruce Fischl2,3,4

  • 1Division of Biomedical Imaging, KTH Royal Institute of Technology, Huddinge, Sweden.

Proceedings. IEEE International Symposium on Biomedical Imaging
|July 9, 2025
PubMed
Summary

This study introduces a new machine learning model for precise rigid brain registration in longitudinal studies. The model achieves superior accuracy for within-subject image alignment compared to existing methods.

Keywords:
deep learninglongitudinal analysisneuroimagingrigid image registration

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Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Machine Learning

Background:

  • Rigid registration aligns image features using translations and rotations.
  • Current machine learning methods excel in cross-subject registration but struggle with longitudinal (within-subject) registration accuracy.
  • Precise alignment is crucial for longitudinal studies.

Purpose of the Study:

  • To develop and optimize a machine learning model for accurate longitudinal, rigid brain registration.
  • To improve upon existing anatomy-aware, acquisition-agnostic affine registration frameworks.
  • To enhance the precision of within-subject image alignment in neuroimaging.

Main Methods:

  • Developed a novel model building on an existing anatomy-aware, acquisition-agnostic affine registration framework.
  • Optimized the model specifically for longitudinal, rigid brain registration.
  • Trained the model using synthetic within-subject image pairs augmented with rigid and subtle nonlinear transformations.

Main Results:

  • The proposed model estimates more accurate rigid transforms than previous cross-subject networks.
  • The model demonstrates robust performance on longitudinal registration pairs.
  • Effective across different magnetic resonance imaging (MRI) contrasts.

Conclusions:

  • The new model significantly improves longitudinal, rigid brain registration accuracy.
  • This advancement is critical for precise within-subject neuroimaging analysis.
  • The model offers robust and accurate alignment for longitudinal MRI studies.