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

Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

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...

You might also read

Related Articles

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

Sort by
Same author

Trait anxiety in young adults is more consistently associated with resting-state EEG microstate transitions than with stationary spectral power.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology·2026
Same author

[An Improved Faster R-CNN Method for Wound Detection].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2026
Same author

Development and evaluation of a machine learning-based risk prediction model for enteral feeding intolerance in sepsis patients.

Frontiers in nutrition·2026
Same author

Identification of Immune-Related Genes in Predicting the Progression of Colitis-Associated Colorectal Cancer: An Integrated Bioinformatics and Experimental Validation Study.

Journal of inflammation research·2026
Same author

Impact of a leadership training programme on nursing leadership, self-efficacy and competency among ICU clinical nurses: a quasi-experimental study in China.

BMC nursing·2026
Same author

The risk of congenital malformations and neonatal outcomes among singletons and twins born after in vitro fertilization/intracytoplasmic sperm injection: a systematic review and Bayesian network meta-regression analysis.

BMC pregnancy and childbirth·2026
Same journal

Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators.

Physics in medicine and biology·2026
Same journal

Corrigendum: Referenceless MR thermometry-a comparison of five methods (2017<i>Phys. Med. Biol</i>.<b>62</b>1-16).

Physics in medicine and biology·2026
Same journal

Corrigendum: Measured and Monte Carlo simulated electron backscatter to the monitor chamber for the varian TrueBeam linac (2016<i>Phys. Med. Biol</i>.<b>61</b>8779).

Physics in medicine and biology·2026
Same journal

Corrigendum: 3D range-modulator for scanned particle therapy: development, Monte Carlo simulations and experimental evaluation (2017<i>Phys. Med. Biol</i>.<b>62</b>7075).

Physics in medicine and biology·2026
Same journal

Recent progress in applications of computing to radiotherapy (ICCR 2016).

Physics in medicine and biology·2026
Same journal

Novel TMS coils designed using an inverse boundary element method.

Physics in medicine and biology·2026
See all related articles

Related Experiment Video

Updated: Jun 18, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

15.7K

DPP: deep phase prior for parallel imaging with wave encoding.

Congcong Liu1,2, Zhuo-Xu Cui3, Sen Jia1

  • 1Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China.

Physics in Medicine and Biology
|April 12, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep neural network for Magnetic Resonance (MR) imaging to accurately estimate background phase in wave encoding, improving image quality and enabling faster scans. The method enhances phase characterization in parallel imaging with virtual conjugate coils.

Keywords:
MR imagingparallel imaginguntrained neural network

More Related Videos

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
08:51

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

Published on: February 19, 2021

9.0K
Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
05:32

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

262

Related Experiment Videos

Last Updated: Jun 18, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

15.7K
Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
08:51

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

Published on: February 19, 2021

9.0K
Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
05:32

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

262

Area of Science:

  • Magnetic Resonance (MR) Imaging
  • Medical Imaging Physics
  • Deep Learning in Medical Diagnostics

Background:

  • Current Magnetic Resonance (MR) parallel imaging with wave encoding faces limitations in accurately characterizing background phase due to reliance on Auto-Calibration Signals (ACS) data.
  • This calibration process restricts comprehensive and precise background phase estimation, impacting overall image quality and acceleration capabilities.

Purpose of the Study:

  • To develop a novel deep neural network model for accurate background phase estimation in MR wave encoding.
  • To integrate virtual conjugate coil (VCC) extension within the deep learning framework to enhance phase characterization.
  • To improve the performance of parallel imaging by overcoming limitations of traditional calibration methods.

Main Methods:

  • A deep neural network model guided by deep phase priors and incorporating virtual conjugate coil (VCC) extension was proposed.
  • The model implicitly characterizes background phase using a decoder convolutional neural network, leveraging phase smoothness and compact support properties.
  • Additional priors, including latent image transmission sparsity and coil sensitivity smoothness, were integrated for wave encoding.

Main Results:

  • The proposed method demonstrated superior performance in background phase and coil sensitivity map (CSM) representation compared to conventional techniques.
  • Achieved optimal quantitative metrics (PSNR/SSIM/NMSE) of 44.1359 dB/0.9863/0.0008 for 4-fold and 41.2074/0.9846/0.0017 for 5-fold acceleration.
  • Showcased generalizability across various undersampling patterns and MRI sequences (T1, T2, T2*), and significantly outperformed competing methods in accelerated phase-sensitive SWI imaging.

Conclusions:

  • The developed deep learning approach enables precise background phase characterization within the VCC and wave encoding framework.
  • Empirical findings and theoretical analysis support the method's effectiveness and robustness in accelerating MR imaging.
  • The proposed method offers a significant advancement for accelerated MR imaging, particularly in phase-sensitive applications like SWI.