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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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Updated: Jan 3, 2026

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

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

Published on: November 8, 2012

Synthesizing diffusion tensor imaging from functional MRI using fully convolutional networks.

Seong-Jin Son¹, Bo-Yong Park², Kyoungseob Byeon³

¹Department of Electronic and Computer Engineering, Sungkyunkwan University, South Korea; Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science, South Korea; NEUROPHET Inc., South Korea.

Computers in Biology and Medicine

|November 20, 2019

Summary

This summary is machine-generated.

This study introduces a 3D deep learning model to create diffusion-tensor imaging (DTI) from resting-state functional magnetic resonance imaging (fMRI). The novel approach accurately synthesizes DTI, improving white matter analysis from fMRI data.

Keywords:

Deep learning Diffusion tensor imaging Fully convolutional network Functional MRI Image synthesis

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Area of Science:

Medical imaging
Artificial intelligence
Neuroscience

Background:

Medical image synthesis offers potential for reduced scanning times and efficient data collection.
Resting-state functional magnetic resonance imaging (fMRI) can provide signals from white matter (WM) for assessing alterations.

Purpose of the Study:

To propose a 3D deep learning architecture for synthesizing diffusion-tensor imaging (DTI) from fMRI.
To leverage fMRI signals and T1-weighted images for accurate DTI synthesis.

Main Methods:

Developed a 3D fully convolutional network (FCN) architecture.
Utilized fMRI voxel correlation patterns and T1-weighted images as inputs.
Trained and tested the model on a large-scale open database (n=648 training, n=293 testing).

Main Results:

Achieved an average correlation of 0.808 between synthesized and actual DTI in 38 WM regions.
Significantly improved upon existing methods (r=0.480).
Demonstrated higher correlation with actual DTI compared to conventional machine learning methods.

Conclusions:

Successfully synthesized DTI images from fMRI using a 3D FCN.
The method shows promise for enhancing white matter analysis.
Future work aims to synthesize other imaging modalities from single sources.