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

Towards Automated Spine Fracture Detection on Whole-Body CT of Polytraumatized Patients.

Journal of imaging·2026

Same author

Automated Aortic Quantification Based on Artificial Intelligence: Validation Using Contrast-Enhanced and Non-Contrast CT Scans from the Same Session.

Bioengineering (Basel, Switzerland)·2026

Same author

Structure-function multilayer network integration and cognition in multiple sclerosis.

Network neuroscience (Cambridge, Mass.)·2026

Same author

Decreased Tissue Sodium Concentration in Suspected Prostate Cancer Detected by Internal-Reference <sup>23</sup>Na MRI: A Prospective Exploratory Study.

Diagnostics (Basel, Switzerland)·2026

Same author

Renal Clinical Study Participants Support Data Sharing and Use of Artificial Intelligence.

Kidney international reports·2026

Same author

Adaptive Aggregation of Monte Carlo Augmented Decomposed Filters for Efficient Group-Equivariant Convolutional Neural Network.

IEEE transactions on pattern analysis and machine intelligence·2026

Search research articles

Related Experiment Video

Updated: Nov 18, 2025

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

Accelerated white matter lesion analysis based on simultaneous and quantification using magnetic resonance

Ingo Hermann^1,2, Eloy Martínez-Heras³, Benedikt Rieger¹

¹Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.

Magnetic Resonance in Medicine

|February 6, 2021

Summary

Deep learning accelerates magnetic resonance fingerprinting (MRF) for quantitative mapping of T1 and T2 values. This novel pipeline significantly reduces processing time for assessing white matter lesions in multiple sclerosis patients.

Keywords:

deep learning reconstruction magnetic resonance fingerprinting

More Related Videos

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Related Experiment Videos

Last Updated: Nov 18, 2025

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

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Quantitative magnetic resonance fingerprinting (MRF) offers robust T1 and T2 mapping.
Traditional MRF postprocessing is time-consuming, limiting clinical application.
Accurate assessment of white matter lesions is crucial for multiple sclerosis (MS) management.

Purpose of the Study:

To develop an accelerated postprocessing pipeline for quantitative MRF.
To enhance the efficiency and reproducibility of white matter lesion assessment.
To integrate deep learning with MRF for faster parametric mapping.

Main Methods:

Acquired whole-brain MRF data (EPI scans) with varying TR/TE in 50 MS patients and 10 controls.
Applied distortion correction and denoising to MRF T1 and T2 parametric maps.
Trained a convolutional neural network (CNN) to reconstruct parametric maps and tissue probability maps.

Main Results:

Deep learning reduced postprocessing time from hours to seconds.
High accuracy, reliability, and precision were maintained.
Mean absolute error (5.6% deviation) and log-cosh loss (6.0% deviation) optimized T1 and T2 reconstruction, respectively.

Conclusions:

MRF is a fast and robust tool for quantitative T1 and T2 mapping.
Deep learning significantly accelerates MRF reconstruction and postprocessing.
This approach facilitates efficient and reproducible white matter lesion assessment.