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

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
Gradient Fields01:27

Gradient Fields

A gradient field is a vector field derived from a scalar field. A scalar field assigns a single numerical value to every point in space, such as temperature, pressure, or electric potential. The gradient field describes how that value changes from point to point. It gives both the direction of the fastest increase and the rate of change in that direction.For a scalar field f(x, y), the gradient is written as\begin{equation*}\nabla f=\left\langle \jfrac{\partial f}{\partial x},\jfrac{\partial...

You might also read

Related Articles

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

Sort by
Same author

Standardized Reporting of Cardiac Magnetic Resonance Examinations in Children With Cardiac Diseases and Adults With Congenital Heart Disease: A Scientific Statement From the Association for European Pediatric and Congenital Cardiology (AEPC) and the International Society for Magnetic Resonance in Medicine (ISMRM).

Journal of magnetic resonance imaging : JMRI·2026
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

Isotropic three-dimensional cardiac cine imaging at 0.55T using stack-of-spiral sampling and four-dimensional iterative motion compensation.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2026
Same author

MRI and Implant Safety at Low-Field and Ultralow-Field Strengths.

Journal of magnetic resonance imaging : JMRI·2025

Related Experiment Video

Updated: Jun 20, 2026

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

MaxGIRF: Image reconstruction incorporating concomitant field and gradient impulse response function effects.

Nam G Lee1, Rajiv Ramasawmy2, Yongwan Lim3

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

Magnetic Resonance in Medicine
|April 21, 2022
PubMed
Summary

A new MaxGIRF method improves non-Cartesian MRI by correcting off-resonance, trajectory, and concomitant field errors simultaneously. This advanced reconstruction mitigates blurring artifacts in phantoms and in vivo imaging.

Keywords:
MRI reconstructionconcomitant fieldsexpanded signal modelgradient distortiongradient impulse response function

More Related Videos

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers
08:33

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers

Published on: January 5, 2024

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Related Experiment Videos

Last Updated: Jun 20, 2026

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers
08:33

A Randomized, Sham-Controlled Trial of Cranial Electrical Stimulation for Fibromyalgia Pain and Physical Function, Using Brain Imaging Biomarkers

Published on: January 5, 2024

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Area of Science:

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

Background:

  • Non-Cartesian MRI sequences are susceptible to artifacts from concomitant field effects.
  • Accurate image reconstruction is crucial for diagnostic quality in MRI.

Purpose of the Study:

  • To develop and evaluate MaxGIRF, a novel reconstruction strategy for compensating concomitant field effects in non-Cartesian MRI.
  • To simultaneously correct off-resonance, concomitant fields, and trajectory errors without specialized hardware.

Main Methods:

  • Introduced MaxGIRF, a higher-order reconstruction method using gradient impulse response functions.
  • Estimated spatiotemporally varying concomitant fields using analytic expressions and gradient waveforms.
  • Applied MaxGIRF to phantom simulations and in vivo human imaging at 0.55 T.

Main Results:

  • MaxGIRF successfully mitigated blurring artifacts in both phantom and in vivo scans.
  • The method demonstrated superiority over existing techniques, particularly in challenging regions.
  • A low-rank approximation of MaxGIRF achieved <2% error with reduced computational cost.

Conclusions:

  • MaxGIRF offers simultaneous correction for off-resonance, trajectory errors, and concomitant field effects in non-Cartesian MRI.
  • The method's impact is most significant with longer readouts or lower field strengths.