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

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...

You might also read

Related Articles

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

Sort by

Same author

Brain Health in Former Professional Motorsport Drivers: Case Series in a Population Exposed to Sport-Related Concussions and Repetitive Head Impacts.

Sports medicine - open·2026

Same author

Inter-individual alignment of multimodal brain networks with anatomical constraints.

Network neuroscience (Cambridge, Mass.)·2026

Same author

Characterizing dynamic functional connectivity subnetwork contributions in narrative classification with Shapley values.

Network neuroscience (Cambridge, Mass.)·2025

Same author

Fluid-Suppressed Amide Proton Transfer-Weighted Imaging Outperforms Leakage-Corrected Dynamic Susceptibility Contrast Perfusion in Distinguishing Progression from Radionecrosis in Brain Metastases.

Cancers·2025

Same author

The effect of common parameters of bipolar stimulation on brain evoked potentials.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2024

Same author

Mapping functional traces of opioid memories in the rat brain.

Brain communications·2024

Same journal

HiVLR: Hierarchical Vision-Language Reasoning for interpretable zero-shot radiography image understanding.

Medical image analysis·2026

Same journal

FAA-Net: Fetal abdominal anomaly diagnosis in prenatal ultrasound via LLM-enhanced multi-instance learning.

Medical image analysis·2026

Same journal

Wavelet-inspired diffusion model with near-field constraint for real-time echocardiography dehazing.

Medical image analysis·2026

Same journal

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026

Same journal

MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs.

Medical image analysis·2026

Same journal

Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy.

Medical image analysis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 2, 2026

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

A computational Framework for generating rotation invariant features and its application in diffusion MRI.

Mauro Zucchelli¹, Samuel Deslauriers-Gauthier¹, Rachid Deriche¹

¹Athena Project-Team, Inria Sophia Antipolis - Méditerranée, Université Côte D'Azur, France.

Medical Image Analysis

|December 7, 2019

Summary

This summary is machine-generated.

We developed a computational framework to generate Rotation Invariant Features (RIF) for spherical functions. These new RIFs offer advanced analysis of diffusion MRI data for brain microstructure estimation.

Keywords:

Biomarkers Diffusion MRI Gaunt coefficients Rotation invariants Spherical harmonics

More Related Videos

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

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Related Experiment Videos

Last Updated: Jan 2, 2026

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

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

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Area of Science:

Computational mathematics
Medical imaging analysis
Neuroscience

Background:

Spherical functions are crucial for analyzing data on a sphere, common in fields like medical imaging.
Existing rotation invariant features have limitations in capturing the full complexity of spherical data.
Spherical Harmonic (SH) representations are widely used but require robust invariant features for analysis.

Purpose of the Study:

To introduce a novel computational framework for analytically generating a complete set of algebraically independent Rotation Invariant Features (RIF).
To provide closed-form solutions for new invariants that expand upon existing measures like spherical mean, power-spectrum, and bispectrum.
To demonstrate the utility of these RIFs in modeling and analyzing diffusion MRI (dMRI) data for brain microstructure.

Main Methods:

Developed a computational framework to derive Rotation Invariant Features (RIF) from Laplace-series expansion of spherical functions.
Derived closed-form solutions for a complete set of algebraically independent RIFs.
Applied the framework to model dMRI signals, including Apparent Diffusion Coefficient (ADC), diffusion signal, and fiber Orientation Distribution Function (fODF).

Main Results:

Generated a novel set of algebraically independent RIFs with closed-form solutions.
Established links between new RIFs and statistical/geometrical measures (mean, variance, volume) of spherical signals.
Demonstrated successful application in dMRI signal modeling and microstructure estimation using synthetic and real data.

Conclusions:

The proposed framework provides a powerful tool for generating comprehensive Rotation Invariant Features.
These RIFs enhance the analysis of spherical data, particularly in dMRI for brain microstructure recovery.
The framework offers greater flexibility and potential for innovative developments in microstructure imaging.