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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: May 22, 2025

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

Implicit neural representation of multi-shell constrained spherical deconvolution for continuous modeling of

Tom Hendriks¹, Anna Vilanova¹, Maxime Chamberland¹

¹Department of Computer Science and Mathematics, Eindhoven University of Technology, AP Eindhoven, The Netherlands.

Imaging Neuroscience (Cambridge, Mass.)

|March 13, 2025

Summary

This study introduces an implicit neural representation (INR) for multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) in diffusion MRI. This machine learning approach creates continuous brain fiber models, improving accuracy and leveraging spatial correlations.

Keywords:

FODs INR constrained spherical deconvolution fiber orientation distribution functions implicit neural representation multi-shell diffusion MRI

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Published on: April 7, 2015

Area of Science:

Neuroimaging
Computational Neuroscience
Biomedical Engineering

Background:

Diffusion MRI (dMRI) reveals brain microstructure and macrostructure.
Multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) models fiber orientation distributions (FODs) from dMRI data.
Voxel-wise MSMT-CSD is susceptible to noise, lacks spatial regularization, and requires costly interpolation.

Purpose of the Study:

To develop a novel unsupervised machine learning framework using implicit neural representations (INRs) for dMRI analysis.
To generate a continuous representation of dMRI datasets, overcoming limitations of voxel-wise methods.
To improve the accuracy and efficiency of modeling brain fiber orientation distributions.

Main Methods:

Applied implicit neural representation (INR) methodology to the MSMT-CSD model.
Developed an unsupervised machine learning framework generating a continuous representation of dMRI data.
Input coordinates into the INR to obtain spherical harmonics coefficients parameterizing FODs at any location.

Main Results:

The INR framework generates continuous FOD representations of dMRI datasets.
The model leverages spatial correlations between voxels, acting as a regularization technique.
Quantitative and qualitative evaluations on synthetic and real dMRI data demonstrate competitive performance.

Conclusions:

Implicit neural representations offer a powerful, continuous alternative to voxel-wise methods for dMRI analysis.
This approach enhances parameter estimation by utilizing spatial information and reducing noise impact.
The INR-based MSMT-CSD framework shows promise for more accurate and efficient brain microstructure modeling.