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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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An Automated Tool to Classify and Transform Unstructured MRI Data into BIDS Datasets.

Alexander Bartnik¹, Sujal Singh¹, Conan Sum¹

¹Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 77 Goodell St, Buffalo, NY, 14203, USA.

Neuroinformatics

|March 26, 2024

Summary

This study introduces an automated method to classify magnetic resonance imaging (MRI) data and organize it into the Brain Imaging Data Structure (BIDS) format. The XGBoost model achieves high accuracy, simplifying neuroimaging data management for clinical research.

Keywords:

Automation BIDS Data Curation Machine Learning Magnetic Resonance Imaging Reproducibility

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Area of Science:

Neuroimaging
Data Science
Medical Informatics

Background:

Large neuroimaging datasets are crucial for clinical research.
Inconsistent file naming conventions hinder data organization and analysis.
Manual curation of imaging data is time-consuming and labor-intensive.

Purpose of the Study:

To automate the classification and organization of magnetic resonance imaging (MRI) data.
To transform raw, unstructured MRI images into the Brain Imaging Data Structure (BIDS) format.
To reduce the barrier to entry for clinical scientists using neuroimaging data.

Main Methods:

Trained an XGBoost model to classify MRI acquisition types.
Utilized acquisition parameters stored in image file metadata.
Mapped metadata to BIDS naming conventions for data transformation.

Main Results:

Achieved 99.475% accuracy in classifying MRI acquisition types.
Reported high micro/macro-averaged precision (0.9995/0.994), recall (0.9995/0.989), and F1 scores (0.9995/0.991).
Demonstrated accurate and rapid classification and transformation with minimal user intervention.

Conclusions:

The automated approach significantly streamlines the organization of neuroimaging data.
Increased accessibility of existing neuroimaging data for clinical research.
Reduces manual effort, enabling faster and more efficient data analysis.