May 4, 2018

Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters

BioRxiv : the Preprint Server for Biology
Suheyla Cetin KarayumakYogesh Rathi

Abstract

A joint and integrated analysis of multi-site diffusion MRI (dMRI) datasets can dramatically increase the statistical power of neuroimaging studies and enable comparative studies pertaining to several brain disorders. However, dMRI data sets acquired on multiple scanners cannot be naively pooled for joint analysis due to scanner specific nonlinear effects as well as differences in acquisition parameters. Consequently, for joint analysis, the dMRI data has to be harmonized, which involves removing scanner-specific differences from the raw dMRI signal. In this work, we present a dMRI harmonization method that, when applied to multi-site data, is capable of removing scanner-specific effects, while accounting for minor differences in acquisition parameters such as b-value, spatial resolution and number of gradient directions in the dMRI data (typical for multi-site clinical research scans). We validate our algorithm on dMRI data acquired from two sites: Philadelphia Neurodevelopmental Cohort (PNC) with 800 healthy adolescents (ages 8 to 22 years) and Brigham and Women's Hospital (BWH) with 70 healthy subjects (ages 14 to 54 years). In particular, we show that gender differences and maturation in different age groups are preserved a...Continue Reading

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Mentioned in this Paper

Brain Diseases
Size
MRNA Maturation
Heart Disease in Women
Functional Neuroimaging
Spatial Distribution
Diffusion Magnetic Resonance Imaging
Biologic Development
Neurodevelopmental Disorders
Joints

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