Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Restriction-Weighted Q-Space Trajectory Imaging (ResQ): Toward Mapping Diffusion-Time Effects With Tensor-Valued Diffusion Encoding in Human Prostate Cancer Xenografts.

NMR in biomedicine·2026

Same author

The impact of pain in children and adolescents with cerebral palsy: A daily diary study.

Acta psychologica·2026

Same author

Concomitant Gradient Effects Across Field Strengths and Gradient Amplitudes: Improved Estimation of Errors and Correction of Concomitant Dephasing and Diffusion Weighting.

Magnetic resonance in medicine·2026

Same author

Microstructure imaging of prostate cancer by diffusion MRI.

Magma (New York, N.Y.)·2026

Same author

The Role of Dendritic Spines in Water Exchange Measurements With Diffusion MRI: Double Diffusion Encoding and Free-Waveform MRI.

NMR in biomedicine·2026

Same author

Tensor-Valued Diffusion MRI for Microstructural Assessment During Stereotactic Radiotherapy of Brain Metastases: A Feasibility Study.

Tomography (Ann Arbor, Mich.)·2026

Same journal

Intraoperative contrast-enhanced ultrasound features of progressive multifocal leukoencephalopathy: a case report.

Frontiers in neuroimaging·2026

Same journal

SliceMap: a binary classification-driven 2D pipeline for detecting discriminative candidate regions in brain MRI.

Frontiers in neuroimaging·2026

Same journal

Pulvinar pathways as skip connections in deep neural networks for vision.

Frontiers in neuroimaging·2026

Same journal

Evaluating the methodological quality of coordinate-based meta-analyses: the qual-CBMA checklist.

Frontiers in neuroimaging·2026

Same journal

Imaging research, diagnosis, and treatment advances of post-stroke cognitive impairment.

Frontiers in neuroimaging·2026

Same journal

Anatomically constrained volumetric smoothing enhances fMRI reliability while avoiding smoothing artifacts.

Frontiers in neuroimaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 26, 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

Spherical convolutional neural networks can improve brain microstructure estimation from diffusion MRI data.

Leevi Kerkelä¹, Kiran Seunarine^1,2, Filip Szczepankiewicz³

¹UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.

Frontiers in Neuroimaging

|March 29, 2024

Summary

This summary is machine-generated.

Rotationally invariant spherical convolutional neural networks enhance microstructural parameter estimation in diffusion MRI. This machine learning approach improves accuracy and reduces variance compared to traditional methods for brain tissue analysis.

Keywords:

MRI diffusion magnetic resonance imaging geometric deep learning microstructure spherical convolutional neural network

More Related Videos

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Published on: August 11, 2016

Related Experiment Videos

Last Updated: Jun 26, 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

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Published on: August 11, 2016

Area of Science:

Neuroimaging
Biophysics
Machine Learning

Background:

Diffusion magnetic resonance imaging (dMRI) measures water diffusion in brain tissue.
Estimating microstructural properties from dMRI signals is a complex inverse problem.
Machine learning offers potential solutions for improving dMRI-based estimations.

Purpose of the Study:

To evaluate the efficacy of rotationally invariant spherical convolutional neural networks (RISCNNs) for microstructural parameter estimation in dMRI.
To compare the performance of RISCNNs against established methods like the spherical mean technique (SMT) and multi-layer perceptrons (MLPs).

Main Methods:

Trained a RISCNN on simulated noisy dMRI data to predict ground-truth microstructural parameters.
Applied the trained network to clinical dMRI data to generate microstructural parameter maps.
Utilized both two-compartment and three-compartment models for parameter estimation.

Main Results:

The RISCNN demonstrated superior performance compared to SMT and MLPs.
Achieved higher prediction accuracy than SMT.
Exhibited less rotational variance than MLPs.
Successfully estimated parameters for a three-compartment model, including apparent neural soma density.

Conclusions:

RISCNNs represent a significant advancement in microstructural parameter estimation from dMRI data.
The developed network and pipeline are generalizable to various Gaussian compartment models.
This approach holds promise for enhanced clinical and scientific applications of dMRI.