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

You might also read

Related Articles

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

Sort by
Same author

Magnetic Resonance Spectroscopy Deep Learning with Magnetic Resonance Background Generator Enables In Vivo Metabolite Quantification of Hepatic Encephalopathy.

IEEE transactions on bio-medical engineering·2026
Same author

Cable bacteria drive electrochemical coupling and elemental cycling in rhizosphere: A review.

Ying yong sheng tai xue bao = The journal of applied ecology·2026
Same author

Atomically confined insertion for 2D strain and polarization engineered GaN electronics.

Nature communications·2026
Same author

Efficacy of tranexamic acid for prevention of heterotopic ossification after orthopedic surgery: a systematic review and meta-analysis.

BMC surgery·2026
Same author

Donor-Acceptor-Donor Type Diimidazole-Based Metal-Organic Framework for Photocatalytic C-O and C-C Bond Formation.

Inorganic chemistry·2026
Same author

An Electrical Capacitance Tomography Dataset for Image Reconstruction Benchmarking.

Scientific data·2026
Same journal

Generative morphodynamic forecasting enables robust zero-shot volumetric medical segmentation.

Medical image analysis·2026
Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026
Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026
Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026
Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026
Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026
See all related articles

Related Experiment Video

Updated: Nov 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

26.6K

A guaranteed convergence analysis for the projected fast iterative soft-thresholding algorithm in parallel MRI.

Xinlin Zhang1, Hengfa Lu1, Di Guo2

  • 1Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen 361005, China.

Medical Image Analysis
|February 15, 2021
PubMed
Summary
This summary is machine-generated.

This study analyzes the parallel imaging pFISTA algorithm for faster Magnetic Resonance Imaging (MRI) reconstructions. We provide convergence guarantees and recommended step sizes for SENSE and SPIRiT, improving MRI data acquisition.

Keywords:
Convergence analysisImage reconstructionParallel imagingpFISTA

More Related Videos

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

13.2K
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

28.9K

Related Experiment Videos

Last Updated: Nov 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

26.6K
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

13.2K
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

28.9K

Area of Science:

  • Medical Imaging
  • Computational Science

Background:

  • Magnetic Resonance Imaging (MRI) faces lengthy data acquisition challenges.
  • Sparse sampling and parallel imaging accelerate MRI but require efficient reconstruction algorithms.
  • The pFISTA algorithm is effective for parallel imaging, but its convergence is not well-defined.

Purpose of the Study:

  • To provide guaranteed convergence analysis for the parallel imaging pFISTA algorithm.
  • To address the open question of convergence criteria for parallel imaging pFISTA.
  • To offer practical guidance on step size selection for SENSE and SPIRiT reconstructions.

Main Methods:

  • Developed guaranteed convergence analysis for the parallel imaging pFISTA algorithm.
  • Applied the analysis to SENSE and SPIRiT parallel imaging reconstruction models.
  • Conducted experiments on in vivo brain images to validate the convergence criterion.

Main Results:

  • Established guaranteed convergence for the parallel imaging pFISTA algorithm.
  • Provided recommended step size values for SENSE and SPIRiT.
  • Experimental results confirmed the validity of the proposed convergence criterion.

Conclusions:

  • The convergence analysis offers a clear criterion for parallel imaging pFISTA.
  • Recommended step sizes facilitate faster and more reliable MRI reconstructions.
  • This work simplifies the application of pFISTA in parallel MRI.