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Magnetic Resonance Imaging

Magnetic Resonance Imaging

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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Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

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Deep Learning-Driven Transformation: A Novel Approach for Mitigating Batch Effects in Diffusion MRI Beyond

Akihiko Wada¹, Toshiaki Akashi¹, Akifumi Hagiwara¹

¹Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.

Journal of Magnetic Resonance Imaging : JMRI

|October 25, 2023

Summary

This study developed a deep learning (DL) model to reduce variations in diffusion-weighted images (DWIs) from different MRI scanners. The DL approach enhances image quality and improves the generalizability of DL models in medical imaging.

Keywords:

batch effect mitigation deep learning diffusion MRI image diversity

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

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Magnetic Resonance (MR) image "batch effects" from varying scanner hardware and parameters compromise image quality.
These variations hinder the generalizability of deep learning (DL) models used in medical image analysis.

Purpose of the Study:

To develop a DL model for contrast adjustment and super-resolution to standardize diffusion-weighted images (DWIs).
The goal is to reduce diversity in DWIs caused by different magnetic field strengths and imaging parameters.

Main Methods:

A DL model was trained and validated on a dataset of 1134 adult subjects using data from seven MR scanners (1.5T and 3T).
The model employed contrast adjustment and super-resolution techniques to harmonize DWI data.
Evaluation involved radiologist assessment, image quality metrics (PSNR, SSIM), texture analysis, and a ResNet-50 model performance comparison.

Main Results:

The DL protocol successfully reduced variations in DWI contrast and resolution across different MR devices.
Performance metrics for a ResNet-50 model showed a significant decrease in accuracy, precision, recall, and F1 score after harmonization, indicating reduced machine-specific bias.
t-SNE visualization confirmed improved feature consistency across scanners, and the autoencoder halved learning iterations with a Dice coefficient >0.74 for lesion signal reproducibility.

Conclusions:

The developed DL strategy effectively mitigates batch effects in diffusion MR images.
This approach improves the quality and generalizability of MR images for DL applications in radiology.