Integrated diffusion image operator (iDIO): A pipeline for automated configuration and processing of diffusion MRI data

Author:

Hsu Chih‐Chin Heather12ORCID,Chong Shin Tai1,Kung Yi‐Chia13ORCID,Kuo Kuan‐Tsen1,Huang Chu‐Chung45,Lin Ching‐Po167

Affiliation:

1. Institute of Neuroscience National Yang Ming Chiao Tung University Taipei Taiwan

2. Center for Geriatrics and Gerontology Taipei Veterans General Hospital Taipei Taiwan

3. Department of Radiology Tri‐Service General Hospital, National Defense Medical Center Taipei Taiwan

4. Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science East China Normal University Shanghai China

5. Shanghai Changning Mental Health Center Shanghai China

6. Brain Research Center National Yang Ming Chiao Tung University Taipei Taiwan

7. Medical Device Innovation and Translation Center National Yang Ming Chiao Tung University Taipei Taiwan

Abstract

AbstractThe preprocessing of diffusion magnetic resonance imaging (dMRI) data involve numerous steps, including the corrections for head motion, susceptibility distortion, low signal‐to‐noise ratio, and signal drifting. Researchers or clinical practitioners often need to configure different preprocessing steps depending on disparate image acquisition schemes, which increases the technical threshold for dMRI analysis for nonexpert users. This could cause disparities in data processing approaches and thus hinder the comparability between studies. To make the dMRI data processing steps transparent and adapt to various dMRI acquisition schemes for researchers, we propose a semi‐automated pipeline tool for dMRI named integrated diffusion image operator or iDIO. This pipeline integrates features from a wide range of advanced dMRI software tools and targets at providing a one‐click solution for dMRI data analysis, via adaptive configuration for a set of suggested processing steps based on the image header of the input data. Additionally, the pipeline provides options for post‐processing, such as estimation of diffusion tensor metrics and whole‐brain tractography‐based connectomes reconstruction using common brain atlases. The iDIO pipeline also outputs an easy‐to‐interpret quality control report to facilitate users to assess the data quality. To keep the transparency of data processing, the execution log and all the intermediate images produced in the iDIO's workflow are accessible. The goal of iDIO is to reduce the barriers for clinical or nonspecialist users to adopt the state‐of‐art dMRI processing steps.

Funder

National Institutes of Health

U.S. Department of Defense

Publisher

Wiley

Subject

Neurology (clinical),Neurology,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology,Anatomy

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