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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development.

Minh Sao Khue Luu1, Margaret V Benedichuk1, Ekaterina I Roppert1

  • 1The Artificial Intelligence Research Center, Novosibirsk State University, 630090 Novosibirsk, Russia.

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Summary

Public brain MRI datasets show significant variability in scale, diversity, and preprocessing, hindering foundation model development. Harmonization alone is insufficient; preprocessing-aware and domain-adaptive strategies are crucial for generalizable models.

Keywords:
brain MRIcovariate shiftdata harmonizationfoundation modelspreprocessing variabilitypublic datasets

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Data Science

Background:

  • Foundation models for brain MRI require large, diverse, and consistent datasets.
  • Systematic assessments of public brain MRI data for foundation model development are lacking.

Purpose of the Study:

  • To systematically assess the scale, diversity, and consistency of publicly available brain MRI datasets.
  • To evaluate the impact of preprocessing variability on data harmonization.
  • To inform the development of generalizable brain MRI foundation models.

Main Methods:

  • Analysis of 54 public brain MRI datasets (538,031 scans) at dataset and image levels.
  • Quantification of image properties (voxel spacing, orientation, intensity) across 14 datasets.
  • Evaluation of preprocessing steps (normalization, bias correction, registration, etc.) and their impact on data.
  • Feature-space analysis using a 3D DenseNet121 to assess residual covariate shift.

Main Results:

  • Significant imbalances exist between large healthy cohorts and smaller clinical populations in public MRI datasets.
  • Substantial heterogeneity in image properties (voxel spacing, orientation, intensity) across datasets.
  • Standardized preprocessing improves within-dataset consistency but leaves residual inter-dataset differences.
  • Residual covariate shift observed even after harmonization, indicating limitations of harmonization alone.

Conclusions:

  • Public brain MRI resources exhibit considerable variability, posing challenges for foundation model development.
  • Harmonization strategies alone are insufficient to overcome inter-dataset bias.
  • Preprocessing-aware and domain-adaptive approaches are essential for creating robust and generalizable brain MRI foundation models.