Confidence intervals for the volume of brain structures in Cavalieri sampling with local errors

Area of Science:

• Neuroscience
• Medical Imaging
• Stereology

Background:

• Estimating brain structure volume is vital for research on neurological disease, development, and aging.
• Design-based stereological methods combined with magnetic resonance (MR) imaging are commonly used.
• The Cavalieri method estimates volume from MR images, but predicting estimator precision is challenging due to spatial data dependence.

Purpose of the Study:

• To develop a method for constructing confidence intervals for volume estimation in brain research.
• To analyze variability at different sampling levels (between and within sections).
• To validate the new approach through simulations and application to a real-world case.

Main Methods:

• Utilizing the Cavalieri method with equidistant MR images.
• Implementing a second sampling level (e.g., point counting) for section area estimation.
• Developing a confidence interval by analyzing inter- and intra-section variability.

Main Results:

• A novel method for calculating confidence intervals for volume estimation was developed.
• Simulations confirmed the validity of the proposed methodology.
• The approach was successfully applied to assess hippocampus volume changes in an epilepsy patient.

Conclusions:

• The developed method enhances the precision of brain structure volume estimation using MR imaging.
• This technique provides a reliable way to quantify morphological alterations in neurological conditions.
• Accurate volume quantification is essential for advancing brain research and clinical diagnostics.