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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

290
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
290
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

242
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
242

You might also read

Related Articles

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

Sort by
Same author

Imaging Near Spinal Fixation Hardware at 0.55 T Compared With 3 T.

Journal of magnetic resonance imaging : JMRI·2026
Same author

Improved dynamic MRI of the wrist and heart at 0.55 T enabled by rapid 3D printed flexible coils.

Nature communications·2026
Same author

Dynamic Mode Decomposition (DMD) for Low-Latency Real-Time Cardiac MRI.

Magnetic resonance in medicine·2026
Same author

Open-Source Simulator of Imaging Near Metal at Arbitrary Magnetic Field Strengths.

Magnetic resonance in medicine·2025
Same author

Online Spatiotemporally Constrained Reconstruction for Real-Time Interactive MRI.

Magnetic resonance in medicine·2025
Same author

Acceleration of slice encoding for metal artifact correction at 0.55 T using hexagonal sampling.

Magnetic resonance in medicine·2025
Same journal

A Comparison of Tissue Property Values Estimated Using Conventional Cardiac MRF and MT-Cardiac MRF.

Magnetic resonance in medicine·2026
Same journal

Dependence of the Extra-Cellular Diffusion Coefficient on the Fractions of Neurites and Cell Bodies in Gray Matter.

Magnetic resonance in medicine·2026
Same journal

Triple-Pulse <sup>23</sup>Na MRI Sequence (TriNa) for Simultaneous Acquisition of Spin-Density-Weighted and Fluid-Attenuated Images.

Magnetic resonance in medicine·2026
Same journal

Evaluation of Phantom Doping Materials in Quantitative Susceptibility Mapping.

Magnetic resonance in medicine·2026
Same journal

Design of an 8-Channel Transmit 32-Channel Receive 11.7T Head Coil and Evaluation of SNR Gains.

Magnetic resonance in medicine·2026
Same journal

The Potential for Absolute Temperature Imaging Based on Brain Metabolites Using an FID-Shifting Approach in Gradient Echo Planar Spectroscopic Imaging (GREPSI).

Magnetic resonance in medicine·2026
See all related articles

Related Experiment Video

Updated: Jan 16, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.6K

Cartesian MaxGIRF: Model-based EPI reconstruction incorporating gradient nonlinearity and concomitant field effects.

Nam G Lee1, Sophia X Cui2, Krishna S Nayak1,3

  • 1Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA.

Magnetic Resonance in Medicine
|October 3, 2025
PubMed
Summary
This summary is machine-generated.

A new Cartesian MaxGIRF framework corrects EPI distortions from gradient nonlinearity and concomitant fields without spatial blurring. This advanced method improves image quality and mitigates artifacts in MRI scans.

Keywords:
concomitant fieldsecho planar imaginggradient nonlinearityhigher‐order image reconstructionmodel‐based reconstructionoff‐resonance correction

More Related Videos

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

12.2K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.9K

Related Experiment Videos

Last Updated: Jan 16, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.6K
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

12.2K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.9K

Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Medical Physics
  • Image Reconstruction

Background:

  • Lower field strength MRI scanners often suffer from EPI distortions caused by gradient nonlinearity and concomitant fields.
  • Existing correction methods, relying on image-domain interpolation, can lead to undesirable spatial blurring, compromising image quality.
  • These distortions are particularly problematic in scanners with large bore sizes, complex geometries, or strong gradient systems.

Purpose of the Study:

  • To introduce a novel model-based EPI reconstruction framework, Cartesian MaxGIRF, designed to simultaneously compensate for EPI distortions.
  • To avoid the spatial blurring typically introduced by conventional correction techniques.
  • To address distortions arising from concomitant fields, gradient nonlinearity, and off-resonance effects during image reconstruction.

Main Methods:

  • The Cartesian MaxGIRF framework was developed for model-based EPI reconstruction.
  • Performance was evaluated against standard correction methods using phantom and in-vivo human brain datasets at 0.55T.
  • Specific EPI artifacts, including parabolic shift and slice-dependent Nyquist ghost, were investigated and addressed using different EPI sequences (3D GRE-EPI).

Main Results:

  • The proposed framework successfully mitigated parabolic shifts and slice-dependent Nyquist ghosts, artifacts induced by concomitant fields.
  • Cartesian MaxGIRF demonstrated superior retention of image details compared to standard methods when correcting geometric distortions.
  • Theoretical analysis confirmed the mitigation of parabolic shifts across various imaging scenarios.

Conclusions:

  • The Cartesian MaxGIRF framework effectively mitigates EPI distortions from concomitant fields, gradient nonlinearity, and static off-resonance simultaneously.
  • This approach is particularly beneficial for correcting artifacts caused by second-order concomitant fields in both symmetric and asymmetric gradient systems.
  • The method offers an improved solution for enhancing image quality in EPI MRI without introducing spatial blurring.