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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Calibration of MRI-based reference intervals to new samples.

Andrew A Chen1, Jakob Seidlitz2,3,4,5, Margaret Gardner2,6

  • 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States.

Imaging Neuroscience (Cambridge, Mass.)
|July 14, 2026
PubMed
Summary

A new method, reference interval calibration via conFormal prediction (ReForm), adjusts brain magnetic resonance imaging (MRI) reference intervals for new data without sharing sensitive patient information. ReForm ensures reliable interval calibration, maintaining performance comparable to or better than existing methods.

Keywords:
Alzheimer’s diseasebrain chartscortical thicknessreference intervalsstructural MRI

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Area of Science:

  • Neuroimaging
  • Biostatistics
  • Medical Informatics

Background:

  • Reference intervals are crucial for interpreting brain magnetic resonance imaging (MRI) data.
  • Existing methods struggle when reference data and new samples differ in acquisition or processing.
  • Brain charts provide reference intervals but face challenges with data heterogeneity.

Purpose of the Study:

  • To develop a novel method for calibrating MRI reference intervals for new samples.
  • To address the challenge of differing data characteristics between reference and new datasets.
  • To ensure privacy by avoiding the need to share reference data.

Main Methods:

  • Proposed reference interval calibration via conFormal prediction (ReForm).
  • Leveraged conformal prediction for guaranteed interval coverage.
  • Compared ReForm against refitting, statistical harmonization, and model-based adjustments using resampling experiments on cortical thickness data.

Main Results:

  • ReForm successfully adjusts reference intervals for new samples.
  • Empirical results show ReForm controls false positive rates (FPR) similarly or better than methods requiring data sharing.
  • ReForm offers a privacy-preserving alternative without compromising interval calibration accuracy.

Conclusions:

  • ReForm provides a robust and privacy-preserving solution for calibrating brain MRI reference intervals.
  • The method is effective even when new data differs significantly from reference data.
  • Recommendations for practical application and an R package are provided.