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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Alzheimer's Disease Research

Background:

  • Accurate measurement of percent brain volume change (PBVC) is crucial for understanding Alzheimer's dementia progression.
  • Conventional methods like SIENA (Structural Image Evaluation, using Normalization, of Atrophy) rely on brain extraction tools (BET) which can impact accuracy.
  • Improving the robustness and reproducibility of PBVC measurements is essential for clinical trials and research.

Purpose of the Study:

  • To enhance the image analysis pipeline for detecting PBVC in Alzheimer's dementia populations.
  • To evaluate the performance of an improved brain extraction mask from BEaST (Brain Extraction based on nonlocal Segmentation Technique) integrated with SIENA.
  • To compare the proposed BEaST-SIENA method against conventional BET-based SIENA approaches using multiple datasets.

Main Methods:

  • Compared five brain extraction methods: four variants of BET and BEaST.
  • Applied SIENA with each brain extraction method to analyze scan-rescan and longitudinal MRIs from ADNI and OASIS datasets.
  • Evaluated scan-rescan reproducibility, cross-sectional error, statistical power (effect size), and correlation of PBVC measurements.

Main Results:

  • BEaST generated consistent brain masks, showing superior scan-rescan reproducibility.
  • The BEaST-SIENA pipeline demonstrated the smallest cross-sectional scan-rescan error in absolute percent brain volume difference (p≤0.0187).
  • BEaST-SIENA achieved the highest statistical power and showed the smallest absolute difference in PBVC between scan and rescan datasets, with the highest correlation.

Conclusions:

  • BEaST is a robust tool for brain extraction, offering consistency and reproducibility.
  • Combining BEaST with SIENA significantly improves the reproducibility and statistical power of percent brain volume change analysis.
  • The BEaST-SIENA pipeline offers a more reliable method for tracking brain atrophy in Alzheimer's disease research.