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Related Experiment Video

Updated: Jul 24, 2025

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
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Brain Parcellation Repeatability and Reproducibility Using Conventional and Quantitative 3D MR Imaging.

J B M Warntjes1,2, P Lundberg3,4, A Tisell3,4

  • 1From the Centre for Medical Image Science and Visualization (J.B.M.W.) marcel.warntjes@cmiv.liu.se.

AJNR. American Journal of Neuroradiology
|July 6, 2023
PubMed
Summary
This summary is machine-generated.

Synthesizing T1-weighted brain images from quantitative MRI data enables automatic brain parcellation. This approach shows high repeatability but requires further optimization to reduce biases across field strengths.

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Area of Science:

  • Radiology
  • Neuroimaging
  • Medical Physics

Background:

  • Automatic brain parcellation typically requires dedicated MRI sequences, increasing examination time.
  • Quantitative MRI (qMRI) sequences can acquire R1, R2, and proton density maps.

Purpose of the Study:

  • To synthesize T1-weighted (T1w) images from qMRI data for brain volume measurement and parcellation.
  • To evaluate the repeatability and reproducibility of brain parcellation using synthetic vs. conventional T1w images.

Main Methods:

  • Twelve subjects underwent 1.5T and 3T MRI scans using 3D-QALAS and conventional T1w sequences.
  • Synthetic T1w images were generated from R1, R2, and proton density maps using SyMRI.
  • Brain parcellation was performed on both conventional and synthetic T1w images using NeuroQuant.

Main Results:

  • High correlations were observed between conventional and synthetic T1w images (medians of 0.97 at 1.5T, 0.92 at 3T).
  • Repeatability was high, with median coefficients of variation of 1.2% (1.5T) and 1.5%-4.4% (3T).
  • Significant biases were noted between conventional and synthetic methods, and across field strengths.

Conclusions:

  • qMRI data can be used to synthesize T1w image stacks for automatic brain parcellation.
  • Further investigation into synthetic parameter settings is needed to minimize observed biases.