Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Relative Motion Analysis - Acceleration

Relative Motion Analysis - Acceleration

A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...

Curvilinear Motion: Rectangular Components

Curvilinear Motion: Rectangular Components

Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...

Relative Motion Analysis using Rotating Axes - Acceleration

Relative Motion Analysis using Rotating Axes - Acceleration

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...

You might also read

Related Articles

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

Sort by

Same author

Volume Encoding Gaussians: Transfer Function-Agnostic 3D Gaussians for Volume Rendering.

IEEE transactions on visualization and computer graphics·2026

Same author

Topology Assisted Clustering of Temporal fMRI Brain Networks With Use-Case in Mitigating Non-Neural Multi-Site Variability.

IEEE access : practical innovations, open solutions·2026

Same author

UALCAN Mobile, an app for cancer proteogenomic data analysis.

Research square·2025

Same author

UALCAN Mobile, an app for cancer proteogenomic data analysis.

bioRxiv : the preprint server for biology·2025

Same author

Diffusion probabilistic generative models for accelerated, in-NICU permanent magnet neonatal MRI.

Magnetic resonance in medicine·2025

Same author

Robust multi-coil MRI reconstruction via self-supervised denoising.

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

Search research articles

Related Experiment Video

Updated: Jun 27, 2025

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

Accelerated motion correction with deep generative diffusion models.

Brett Levac¹, Sidharth Kumar¹, Ajil Jalal²

¹Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, USA.

Magnetic Resonance in Medicine

|April 30, 2024

Summary

This summary is machine-generated.

This study introduces a new method using deep generative diffusion models to reconstruct clear MRI images despite subject motion and data acceleration. The technique effectively corrects motion artifacts without external signals, improving image quality.

Keywords:

MRI reconstruction deep generative diffusion models deep learning motion correction

More Related Videos

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

Related Experiment Videos

Last Updated: Jun 27, 2025

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Science

Background:

Accelerated Magnetic Resonance Imaging (MRI) is crucial for reducing scan times.
Subject motion during MRI introduces artifacts, degrading image quality and complicating reconstruction.
Existing motion correction methods often struggle with accelerated data or require external reference signals.

Purpose of the Study:

To develop a robust method for accelerated MRI image reconstruction.
To simultaneously correct for subject motion and forward model imperfections.
To address the ill-posed inverse problem in motion-corrupted MRI data.

Main Methods:

A Bayesian framework utilizing deep generative diffusion models was employed.
The method jointly estimates motion-free images and rigid motion parameters.
Reconstruction is performed on subsampled, motion-corrupted 2D k-space data.

Main Results:

Successfully reconstructed motion-free images from accelerated 2D Cartesian and non-Cartesian MRI scans.
Demonstrated effective motion correction without reliance on external reference signals.
Outperformed existing correction techniques on both simulated and prospectively acquired accelerated data.

Conclusions:

A flexible framework for retrospective motion correction in accelerated MRI was developed.
The proposed method leverages deep generative diffusion models for enhanced reconstruction.
Potential applications extend to correcting other forward model corruptions in MRI.