Intersite brain MRI volumetric biases persist even in a harmonized multisubject study of multiple sclerosis

Kelly A Clark1,2, Carly M O'Donnell1,2, Mark A Elliott3

  • 1Penn Statistics in Imaging and Visualization Endeavor, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Abstract

Insights

Multicenter MRI studies show significant site and scanner biases in brain volume measurements for multiple sclerosis (MS) patients. Advanced statistical methods are crucial for mitigating these biases in multicenter research.

Area of Science:

  • Neuroimaging research
  • Medical imaging analysis
  • Multiple Sclerosis (MS) research

Background:

  • Multicenter studies using diverse MRI scanners are common.
  • Scanner and site variations introduce biases in image-based measures.
  • Assessing these biases is critical for reliable multicenter research.

Purpose of the Study:

  • To evaluate biases in brain volumetry across different MRI scanners and sites.
  • To assess the impact of scanner and site differences on image-based measures in MS patients.
  • To investigate the effectiveness of statistical modeling in mitigating these biases.

Main Methods:

  • Acquired MRI data from 11 MS volunteers across four sites using Siemens and Philips 3T scanners.
  • Performed automated lesion detection and brain/thalamic volumetry, alongside manual delineations.
  • Utilized random-effect and nonparametric modeling to analyze inter- and intra-site differences.

Main Results:

  • Nonparametric modeling revealed site explained over 50% of volume variation, increasing to over 75% with mixed scanner data.
  • Significant inter- and intra-site differences were found in most brain volume estimates (P < .05).
  • A shading artifact affected thalamic volume measurements, showing significant differences between affected and unaffected scans.

Conclusions:

  • Brain volumetry differences persist across MRI scanners even with harmonized protocols.
  • Variance component modeling inadequately explained these differences.
  • Statistical innovations show promise for reducing biases in multicenter MS studies.