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COLLATOR: Consistent spatial-temporal longitudinal atlas construction via implicit neural representation.

Lixuan Chen1, Xuanyu Tian2, Jiangjie Wu2

  • 1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Medical Image Analysis
|December 6, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel implicit neural representation (INR) framework to enhance temporal consistency in longitudinal brain atlases. The method treats inconsistencies as noise, creating more accurate developmental trend analyses.

Keywords:
Image denoisingImplicit neural representationLongitudinal brain atlases

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Longitudinal brain atlases are crucial for studying brain development over time.
  • Conventional atlas construction methods average images at discrete time points, leading to temporal inconsistencies and affecting developmental analysis accuracy.

Purpose of the Study:

  • To propose an implicit neural representation (INR)-based framework to improve temporal consistency in longitudinal brain atlases.
  • To address inaccuracies in brain developmental characteristic analysis caused by temporal inconsistencies in existing atlases.

Main Methods:

  • Formulated longitudinal atlas construction as a 4D (spatial-temporal) image denoising task.
  • Treated temporal inconsistency as noise within an implicit function of spatial-temporal coordinates.
  • Employed a self-supervised denoising approach inspired by Noise2Noise to learn a temporally continuous implicit function from inconsistent data.

Main Results:

  • Significantly improved temporal consistency in longitudinal brain atlases across different MRI modalities.
  • Accurately preserved brain structures while enhancing temporal continuity.
  • Enabled the creation of 4D atlases with higher spatial and temporal resolution due to continuous function generation.

Conclusions:

  • The proposed INR-based framework effectively enhances temporal consistency in longitudinal brain atlases.
  • This method offers a more accurate approach to analyzing brain developmental trends.
  • The continuous nature of the generated atlases allows for higher-resolution developmental modeling.