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

Classification of Skeletal Muscle Relaxants01:28

Classification of Skeletal Muscle Relaxants

2.8K
Skeletal muscle relaxants are a group of drugs that can reduce muscle stiffness and induce temporary paralysis to relieve pain. These agents can act centrally to reduce muscle tone or spasms in painful conditions such as multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), or spinal injuries; they are called antispasmodics or spasmolytics.
Peripherally acting skeletal muscle relaxants interfere with the neurotransmission at the neuromuscular end plate to induce paralysis during...
2.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

154
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
154
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

179
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
179
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

166
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
166
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

353
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
353

You might also read

Related Articles

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

Sort by
Same author

Spinal cord imaging for multiple sclerosis: Advances, priorities, and opportunities.

Multiple sclerosis (Houndmills, Basingstoke, England)·2026
Same author

A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.

NeuroImage. Clinical·2026
Same author

Normative T<sub>1</sub> and T<sub>2</sub> Brain Atlases Across the Adult Lifespan in a Chinese Cohort: Multicenter Quantitative MRI Benchmarks for Ageing and Neurodegenerative Research.

Human brain mapping·2026
Same author

When little brain goes to school: Impact of pedagogy on cerebellar peduncles' development.

Developmental cognitive neuroscience·2026
Same author

Automatic multiple sclerosis lesion segmentation in the spinal cord using 3 T and 7 T MP2RAGE images.

Multiple sclerosis and related disorders·2026
Same author

Achieving Ultra-High Acceleration Rates in 7T MRI Using Combined Controlled Aliasing in Parallel Imaging and Compressed Sensing with Deep-Learning-Based Image Reconstruction.

AJNR. American journal of neuroradiology·2026
Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026
Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026
Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026
Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026
Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026
Same journal

Annotation-efficient medical image segmentation via cross-latent graphs and vector-quantized memory.

Medical image analysis·2026
See all related articles

Related Experiment Video

Updated: Nov 22, 2025

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

19.8K

Model-informed machine learning for multi-component T2 relaxometry.

Thomas Yu1, Erick Jorge Canales-Rodríguez2, Marco Pizzolato3

  • 1Signal Processing Lab 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland.

Medical Image Analysis
|January 10, 2021
PubMed
Summary
This summary is machine-generated.

Model-Informed Machine Learning (MIML) accurately reconstructs T2 distributions from MRI signals, improving myelin water fraction mapping for better Multiple Sclerosis lesion visualization and faster analysis.

Keywords:
relaxometryMachine learningMyelin water imaging

More Related Videos

Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration
05:30

Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration

Published on: May 19, 2023

1.6K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K

Related Experiment Videos

Last Updated: Nov 22, 2025

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

19.8K
Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration
05:30

Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration

Published on: May 19, 2023

1.6K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K

Area of Science:

  • Neuroimaging
  • Biophysics
  • Machine Learning

Background:

  • T2 distribution recovery from multi-echo T2 magnetic resonance (MR) signals is crucial for characterizing tissue microstructure, like myelin water fraction (MWF).
  • Current parametric and non-parametric methods for T2 relaxometry face challenges in accuracy and speed.
  • Accurate MWF mapping is essential for diagnosing and monitoring neurological conditions such as Multiple Sclerosis.

Purpose of the Study:

  • To develop a novel, efficient, and accurate method for T2 distribution reconstruction in brain tissue.
  • To leverage machine learning, specifically a multi-layer perceptron (MLP), combined with biophysical models for improved T2 relaxometry.
  • To validate the proposed Model-Informed Machine Learning (MIML) approach against existing methods using synthetic and real-world MR data.

Main Methods:

  • A multi-layer perceptron (MLP) was trained on an extensive synthetic dataset derived from biophysical models to reconstruct T2 distributions.
  • The Model-Informed Machine Learning (MIML) approach directly outputs T2 distributions from MR signals.
  • MIML was evaluated against Gaussian Mixture Fitting (parametric) and Regularized Non-Negative Least Squares (non-parametric) algorithms.

Main Results:

  • MIML demonstrated superior accuracy and noise robustness in T2 distribution reconstruction on synthetic data.
  • Myelin water fraction maps generated by MIML showed excellent conformity with anatomical scans and histological myelin volume maps.
  • MIML provided superior lesion visualization and localization in Multiple Sclerosis patients, with enhanced contrast between lesions and normal-appearing tissue.
  • MIML achieved significant speed improvements, being 22 to 4980 times faster than non-parametric and parametric methods, respectively.

Conclusions:

  • Model-Informed Machine Learning (MIML) offers a highly accurate and computationally efficient solution for T2 distribution reconstruction in brain MR imaging.
  • MIML enhances the diagnostic utility of myelin water fraction mapping, particularly for visualizing and localizing lesions in Multiple Sclerosis.
  • The proposed method represents a significant advancement in quantitative neuroimaging, enabling faster and more reliable tissue microstructure analysis.