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

Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

2.0K
Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this...
2.0K

You might also read

Related Articles

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

Sort by
Same author

Orbital-Engineered Sn/RuO<sub>2</sub> Nanocatalyst with Self-Regulating Electron Configuration for Durable Chlorine Evolution at Industrial Current Densities.

ACS applied materials & interfaces·2026
Same author

Nanointerface Engineering of Thin-Film Membranes for Predictive Analytical Separation in Complex Salt-Lake Brines.

Analytical chemistry·2026
Same author

The role of macrophage-myofibroblast transition in the pathogenesis of multi-organ fibrosis.

Tissue & cell·2026
Same author

APT MRI Signature for Risk Stratification of Pediatric Medulloblastoma.

Chemical & biomedical imaging·2026
Same author

Correction: Time-dependent diffusion MRI for noninvasive molecular subtype differentiation and biological correlation in breast cancer: emphasizing the emerging three-tier HER2 classification.

Frontiers in oncology·2026
Same author

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

International journal of neural systems·2026
Same journal

Structural MRI Volumetry Index for Differentiation of Progressive Supranuclear Palsy From Parkinson's Disease and Multiple System Atrophy by Automatic Segmentation: A Comparison With Magnetic Resonance Parkinsonism Index.

Journal of magnetic resonance imaging : JMRI·2026
Same journal

Integrating nnU-Net Segmentation and Clinical-Radiomics for Multicenter MRI-Based Assessment of Soft Tissue Sarcoma Grade and Ki-67 Expression.

Journal of magnetic resonance imaging : JMRI·2026
Same journal

Optimization of Respiratory Training Methods for Cardiac Magnetic Resonance Imaging.

Journal of magnetic resonance imaging : JMRI·2026
Same journal

Editorial for "Voxel-Wise Radiomics Habitat Analysis of Post-Treatment Gliomas for Noninvasive Differentiation of True Progression and Pseudoprogression".

Journal of magnetic resonance imaging : JMRI·2026
Same journal

Multiparametric Quantitative MRI of Peripheral Nerves to Differentiate Demyelinating From Axonal Polyneuropathies.

Journal of magnetic resonance imaging : JMRI·2026
Same journal

Mapping Fatty Acid Composition in the Human Knee: Short-Term Repeatability at 3T.

Journal of magnetic resonance imaging : JMRI·2026
See all related articles

Related Experiment Video

Updated: May 6, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.2K

Improving Microstructural Estimation in Time-Dependent Diffusion MRI With a Bayesian Method.

Kuiyuan Liu1, Zixuan Lin1, Tianshu Zheng1

  • 1Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.

Journal of Magnetic Resonance Imaging : JMRI
|May 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian method to improve diffusion MRI model fitting, enhancing accuracy and robustness for better characterization of cellular microstructure in pediatric brain tumors.

Keywords:
Bayesian estimationIMPULSEDgliomamicrostructuretime‐dependent diffusion MRI

More Related Videos

Diffusion Imaging in the Rat Cervical Spinal Cord
10:46

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

11.7K
A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
09:59

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Published on: September 16, 2017

14.1K

Related Experiment Videos

Last Updated: May 6, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.2K
Diffusion Imaging in the Rat Cervical Spinal Cord
10:46

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

11.7K
A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
09:59

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Published on: September 16, 2017

14.1K

Area of Science:

  • Medical Imaging
  • Computational Biology
  • Biophysics

Background:

  • Diffusion MRI (td-dMRI) model fitting is challenging due to complex equations, noise, and limited data.
  • Accurate microstructural parameter estimation is crucial for understanding tissue characteristics.

Purpose of the Study:

  • To introduce a Bayesian methodology for refining microstructural fitting within the IMPULSED model.
  • To optimize prior distributions within the Bayesian framework for improved accuracy.

Main Methods:

  • A retrospective study involving 69 pediatric patients with gliomas.
  • Utilized 3T MRI with oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE) sequences.
  • Compared Bayesian fitting against Non-Linear Least Squares (NLLS) using simulated and experimental data, validated against H&E staining.

Main Results:

  • Bayesian method demonstrated significantly improved accuracy and robustness in simulations, reducing Root Mean Square Error (RMSE) and Standard Deviation (STD).
  • Fewer outliers and reduced errors were observed with the Bayesian approach compared to NLLS.
  • Diagnostic performance for tumor grading was comparable, but the Bayesian method yielded smoother microstructural maps and a marginal improvement in correlation with pathology.

Conclusions:

  • The proposed Bayesian method significantly enhances the accuracy and robustness of IMPULSED model parameter estimation.
  • This approach shows potential clinical utility for characterizing cellular microstructure in pediatric brain tumors.
  • The refined fitting improves the reliability of diffusion MRI-based microstructural analysis.