Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
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
Same author

Gilteritinib in an Isolated CNS Recurrence of FLT3-ITD Positive AML.

Clinical case reports·2026
Same author

seg2med: a bridge from artificial anatomy to multimodal medical images.

Physics in medicine and biology·2025
Same author

Targeting tumoral heterogeneity in lung cancer: a novel, CT-texture-guided targeted biopsy approach with exome sequencing.

NPJ precision oncology·2025

Related Experiment Video

Updated: Jul 4, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

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

40.3K

A generalizable deep voxel-guided morphometry algorithm for the detection of subtle lesion dynamics in multiple

Anish Raj1,2, Achim Gass3,4, Philipp Eisele3,4

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

Frontiers in Neuroscience
|February 9, 2024
PubMed
Summary

A new deep learning algorithm for Voxel-Guided Morphometry (VGM) maps improves multiple sclerosis (MS) brain analysis. This method enhances accuracy in detecting MS disease activity from longitudinal MRI scans.

Keywords:
attention mechanismbrain MRIdeep learninggeneralizabilitylongitudinal change detection mapmultiple sclerosisvoxel-guided morphometry

More Related Videos

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

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

Published on: September 25, 2019

48.1K
Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
08:51

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

Published on: February 19, 2021

9.0K

Related Experiment Videos

Last Updated: Jul 4, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

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

40.3K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

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

Published on: September 25, 2019

48.1K
Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
08:51

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

Published on: February 19, 2021

9.0K

Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Multiple sclerosis (MS) is a chronic neurological disorder causing myelin and axonal loss in the central nervous system.
  • Accurate monitoring of MS-related brain changes is vital for disease management and treatment efficacy.
  • Existing methods require robust algorithms for analyzing longitudinal MRI data.

Purpose of the Study:

  • To develop a generalizable deep learning algorithm for Voxel-Guided Morphometry (VGM) map creation.
  • To analyze MS disease activity using longitudinal MRI brain volumes.
  • To improve the accuracy and robustness of MS lesion detection and monitoring.

Main Methods:

  • Utilized a 3D residual U-Net architecture with attention mechanisms for spatial feature extraction from MRI volumes.
  • Incorporated image normalization techniques (histogram matching, resampling) for enhanced generalization across diverse MRI systems and imaging centers.
  • Analyzed longitudinal 3D T1-weighted MRI data from MS patients acquired across multiple MRI systems.

Main Results:

  • The proposed VGM algorithm demonstrated a 4.3% improvement in mean absolute error (MAE) compared to state-of-the-art (SOTA) methods on the primary dataset.
  • Evaluated on two unseen datasets (n=116), the algorithm achieved an average MAE improvement of 4.2% over SOTA, confirming its generalizability.
  • The approach proved to be fast and robust in analyzing MS patient brain volumes.

Conclusions:

  • The developed deep learning algorithm for VGM mapping offers a significant advancement in analyzing MS disease activity.
  • The model's robustness and generalizability across different MRI systems suggest broad clinical applicability for MS monitoring.
  • This approach has the potential to enhance the evaluation of treatment efficacy and disease progression in multiple sclerosis.