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

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).
Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

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 principle...
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
Magnetic Resonance Imaging01:24

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

Multi-view Chest X-Ray Vision-Language Pre-training via Semantic-Aware Masked Language Modeling and High-order Alignment.

IEEE transactions on medical imaging·2026
Same author

QMSANet: A quaternion multi-scale attention network for robust color image denoising.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Multimodal artificial intelligence in retinopathy of prematurity: A comprehensive narrative review.

Survey of ophthalmology·2026
Same author

Semi-URF: Progressive Uncertainty-Aware Region Filtering and Fusion for Semi-Supervised Medical Image Segmentation.

IEEE journal of biomedical and health informatics·2026
Same author

Structural-Functional Connectome Generation via Diffusion-Guided Graph Transformer for Alzheimer's Disease Analysis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

A fundus image dataset for intelligent diabetic retinopathy system.

Scientific data·2026

Related Experiment Video

Updated: May 18, 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

Diffusion models for brain imaging computing: a survey of frameworks and applications.

Yousuf Babiker M Osman1, Aden Hassan Margani Elsanosi2, Changhong Jing3

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China. yousuf@siat.ac.cn.

Brain Informatics
|May 16, 2026
PubMed
Summary

Diffusion models (DMs) are revolutionizing brain imaging analysis by overcoming challenges in high-dimensional data. This review explores their applications and future directions for scalable, reliable clinical solutions.

Keywords:
Brain decodingBrain imagingDiffusion models

More Related Videos

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Related Experiment Videos

Last Updated: May 18, 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

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Image Computing

Background:

  • High-dimensional brain imaging data presents significant challenges for information extraction due to complexity and noise.
  • Traditional generative models struggle with the intricacies of brain imaging data.
  • Diffusion models (DMs) offer a novel and powerful approach to generative modeling in this domain.

Purpose of the Study:

  • To systematically review the theoretical foundations of diffusion models.
  • To examine the diverse applications of DMs across eight key brain imaging computing tasks.
  • To identify current obstacles and future research directions for DMs in clinical brain imaging.

Main Methods:

  • Comprehensive literature review of diffusion models in brain imaging.
  • Categorization of DM applications into eight distinct neuroimaging tasks.
  • Analysis of practical challenges and future research avenues.

Main Results:

  • Diffusion models demonstrate superior performance over traditional methods in various brain imaging tasks.
  • Key applications include registration, super-resolution, cross-modal synthesis, segmentation, classification, brain network analysis, and BCI.
  • Identified limitations include computational scalability, sampling inefficiency, generalization issues, and multimodal integration challenges.

Conclusions:

  • Diffusion models hold immense potential for advancing brain imaging analysis and clinical applications.
  • Future directions involve integrating DMs with large foundation models for enhanced scalability and reliability.
  • Addressing current hurdles is crucial for the successful clinical translation of DM-based solutions.