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

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.1K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.1K
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

8.3K
Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
8.3K

You might also read

Related Articles

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

Sort by
Same author

Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion.

Computational diffusion MRI : MICCAI Workshop·2017
Same author

Robust Fusion of Diffusion MRI Data for Template Construction.

Scientific reports·2017
Same author

Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2017
Same author

Segmenting hippocampal subfields from 3T MRI with multi-modality images.

Medical image analysis·2017
Same author

Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis.

Machine learning in medical imaging. MLMI (Workshop)·2017
Same author

Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes.

IEEE transactions on medical imaging·2017
Same journal

RGCNN-nnUNet: Recurrent group equivariant nnU-Net for robust brain tissue segmentation on stroke NCCT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Self-supervised isotropic reconstruction for abnormality detection in anisotropic MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

WDBDM: Wavelet-based dual-branch diffusion model for low-dose CT and PET denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

ScribSAM: A robust scribble-supervised framework for spatiotemporal segmentation of breast lesions in ultrasound videos.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Anatomically and biochemically guided deep image prior for sodium MRI denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Segment Anything Model for medical image segmentation: A review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
See all related articles

Related Experiment Video

Updated: Aug 17, 2025

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

26.3K

Does perfect filtering really guarantee perfect phase correction for diffusion MRI data?

Feihong Liu1, Junwei Yang2, Mingyue Feng3

  • 1School of Information Science and Technology, Northwest University, Xi'an, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|December 17, 2022
PubMed
Summary
This summary is machine-generated.

Phase correction for diffusion MRI data can cause signal loss. This study introduces a new phase calibration method using a complex polar coordinate system (CPCS) to fully exploit background phase, significantly reducing signal loss and artifacts.

Keywords:
Adaptive filteringNoise-floorPhase correctionSpatially-varying noise

More Related Videos

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K
Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
05:56

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

1.7K

Related Experiment Videos

Last Updated: Aug 17, 2025

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

26.3K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K
Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
05:56

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

1.7K

Area of Science:

  • Medical Imaging
  • Diffusion MRI Reconstruction
  • Image Signal Processing

Background:

  • Phase correction is used to reconstruct real-valued diffusion MRI data, aiming to avoid noise-floor issues.
  • Existing methods using image filters for phase estimation have reported unexpected signal-loss issues, with unclear causes.
  • Improvements in image filters have partially mitigated signal loss, but advanced filters can introduce severe artifacts.

Purpose of the Study:

  • To identify the underlying reasons for signal loss in phase correction procedures for diffusion MRI.
  • To propose a novel method that addresses the insufficiency of current phase correction techniques.
  • To improve the accuracy and reduce artifacts in diffusion MRI reconstruction, particularly in Fractional Anisotropy (FA) maps.

Main Methods:

  • Introduction of a complex polar coordinate system (CPCS) for detailed analysis of phase correction procedures.
  • Development of a quantitative criterion based on CPCS to ensure sufficient utilization of background phase information.
  • Proposal of a phase calibration procedure to enhance current phase correction methods.

Main Results:

  • The proposed phase calibration method significantly reduces signal loss in reconstructed diffusion MRI data.
  • Artifacts in Fractional Anisotropy (FA) maps are eliminated, leading to improved accuracy.
  • Experimental results on synthetic and real diffusion MRI data validate the effectiveness of the proposed approach.

Conclusions:

  • A perfect image filter is insufficient for perfect phase correction; deeper analysis of phase correction procedures is necessary.
  • The proposed complex polar coordinate system (CPCS) and phase calibration method effectively address signal loss and artifacts.
  • This work offers a significant advancement in diffusion MRI reconstruction, enhancing data quality and quantitative accuracy.