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

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

502
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...
502

You might also read

Related Articles

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

Sort by
Same author

Nasal Instillation of Complex Metal Oxide Particles Induces Brain Metal Accumulation and Neurobehavioral Toxicity in Mice.

Environmental science & technology·2026
Same author

What makes a lonely child: environmental, health, and multimodal neuroimaging correlates of prospective loneliness in the ABCD study.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same author

Stage-Related Alterations in Cortical Functional Connectivity Gradients in Non-Dialysis Patients With Chronic Kidney Disease.

AJNR. American journal of neuroradiology·2026
Same author

Sexual health among patients with breast cancer undergoing endocrine therapy: an integrative review.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026
Same author

Towards a general-purpose foundation model for functional MRI analysis.

Nature biomedical engineering·2026
Same author

Topological disruptions of metabolic brain networks in early-stage chronic kidney disease.

BMC medical imaging·2026

Related Experiment Video

Updated: May 5, 2026

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

28.1K

Cycle-conditional diffusion model for noise correction of diffusion-weighted images using unpaired data.

Pengli Zhu1, Chaoqiang Liu2, Yingji Fu1

  • 1Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong.

Medical Image Analysis
|April 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Cycle-Conditional Diffusion Model (Cycle-CDM) to reduce noise in diffusion-weighted imaging (DWI). The novel method enhances image quality and preserves microstructure for improved medical applications.

Keywords:
Conditional diffusion modelCycle-consistent translationDiffusion weighted imageNoise correctionUnpaired data learning

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

11.6K
Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury
10:33

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

8.4K

Related Experiment Videos

Last Updated: May 5, 2026

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

28.1K
Diffusion Imaging in the Rat Cervical Spinal Cord
10:46

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

11.6K
Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury
10:33

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

8.4K

Area of Science:

  • Medical Imaging
  • Neuroscience
  • Artificial Intelligence

Background:

  • Diffusion-weighted imaging (DWI) is crucial for brain microstructure analysis.
  • DWI signals suffer from noise, leading to low signal-to-noise ratio (SNR) and signal attenuation.
  • Existing methods struggle with noise correction in DWI, limiting its clinical utility.

Purpose of the Study:

  • To develop a novel noise correction method for DWI using unpaired data.
  • To improve the quality and reliability of DWI by reducing noise.
  • To enhance the medical applicability of DWI through advanced denoising techniques.

Main Methods:

  • Proposed a Cycle-Conditional Diffusion Model (Cycle-CDM) utilizing unpaired learning.
  • Employed a cycle-consistent translation architecture to bridge the domain gap between noisy and clean DWIs.
  • Integrated synthesized anatomical priors and specific constraints for accurate denoising while preserving anatomical fidelity.

Main Results:

  • Cycle-CDM demonstrated superior noise correction performance compared to U-Net, CycleGAN, Pix2Pix, MUNIT, and MPPCA.
  • The model effectively generalized to DWIs with head motion across different MRI scanners.
  • Denoised DWI data accurately preserved underlying tissue microstructure, enhancing medical applicability.

Conclusions:

  • Cycle-CDM offers a robust and effective solution for DWI noise reduction.
  • The method's ability to use unpaired data and preserve anatomical details makes it highly valuable for clinical research.
  • This advancement in DWI denoising holds significant potential for improving diagnostic accuracy and understanding of brain microstructure.