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...

Imaging Studies for Cardiovascular System IV: CMRI

Imaging Studies for Cardiovascular System IV: CMRI

Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...

You might also read

Related Articles

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

Sort by

Same author

LFP-Net: A deep learning framework to recognize human behavioral activities using brain STN-LFP signals.

Journal of neuroscience methods·2020

Same author

Studying the Effects of Deep Brain Stimulation and Medication on the Dynamics of STN-LFP Signals for Human Behavior Analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2018

Same author

De-noising of 3D multiple-coil MR images using modified LMMSE estimator.

Magnetic resonance imaging·2018

Same author

A hierarchical structure for human behavior classification using STN local field potentials.

Journal of neuroscience methods·2017

Same author

A Multiple Kernel Learning approach for human behavioral task classification using STN-LFP signal.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2017

Same author

Suppression of noise in SEM images using weighted local hysteresis smoothing filter.

Scanning·2016

Search research articles

Related Experiment Video

Updated: May 11, 2026

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Published on: September 8, 2021

An MRI denoising method using image data redundancy and local SNR estimation.

Hosein M Golshan¹, Reza P R Hasanzadeh, Shahrokh C Yousefzadeh

¹DSP Research Lab, Department of Electrical Engineering, University of Guilan, Rasht, Iran. h.golshan@msc.guilan.ac.ir

Magnetic Resonance Imaging

|May 15, 2013

Summary

This summary is machine-generated.

This study introduces a novel method for 3D Magnetic Resonance Imaging (MRI) denoising, improving image quality by effectively removing Rician noise. The approach enhances anatomical structure preservation in MRI scans.

Keywords:

Denoising Image data redundancy Linear minimum mean square error Magnetic resonance imaging Rician distribution

More Related Videos

Preparation and In Vitro Characterization of Dendrimer-based Contrast Agents for Magnetic Resonance Imaging

Preparation and In Vitro Characterization of Dendrimer-based Contrast Agents for Magnetic Resonance Imaging

Published on: December 4, 2016

Related Experiment Videos

Last Updated: May 11, 2026

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Published on: September 8, 2021

Preparation and In Vitro Characterization of Dendrimer-based Contrast Agents for Magnetic Resonance Imaging

Preparation and In Vitro Characterization of Dendrimer-based Contrast Agents for Magnetic Resonance Imaging

Published on: December 4, 2016

Area of Science:

Medical Imaging
Signal Processing
Biomedical Engineering

Background:

Magnetic Resonance Imaging (MRI) is crucial for diagnostics but susceptible to Rician noise.
Traditional denoising methods using local neighborhoods are suboptimal for 3D MRI data.
Efficiently removing noise while preserving anatomical details is a significant challenge.

Purpose of the Study:

To develop an advanced 3D denoising method for MR images under a Rician noise model.
To improve upon conventional Local Minimum Mean Square Error (LMMSE) estimation by utilizing non-local similar samples.
To enhance the preservation of anatomical structures in denoised MR images.

Main Methods:

A novel LMMSE-based method is proposed, modeling MR data as random fields.
A principled approach selects similar samples from a large data portion, not just local neighborhoods.
An effective similarity measure based on local statistical moments and automatic parameter selection via signal-to-noise ratio (SNR) are employed.
A recursive version of the filter is introduced for enhanced performance.

Main Results:

The proposed method demonstrates superior performance in noise removal compared to state-of-the-art filters.
Experimental results on synthetic and real MR datasets show significant noise reduction.
Anatomical structures are well-preserved, indicating high fidelity in the denoised images.

Conclusions:

The developed LMMSE-based 3D denoising method effectively addresses Rician noise in MR images.
The approach of utilizing non-local similar samples significantly improves denoising efficacy and structure preservation.
This method offers a valuable tool for enhancing the quality and diagnostic utility of MR imaging.