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

Linear Approximation in Time Domain

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...

Imaging Studies III: Computed Tomography

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...

Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

Reconstruction of Signal using Interpolation

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...

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

You might also read

Related Articles

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

Sort by

Same author

Lipid droplet size profiling in yeast.

Biology open·2026

Same author

Zero-Shot Self-Supervised Learning of Single Breath-Hold Magnetic Resonance Cholangiopancreatography (MRCP) Reconstruction.

Magnetic resonance in medicine·2026

Same author

BART Streams: Real-Time Reconstruction Using a Modular Framework for Pipeline Processing.

Magnetic resonance in medicine·2026

Same author

Fast Real-Time Cardiac MRI: a Review of Current Techniques and Future Directions.

Investigative magnetic resonance imaging·2026

Same author

Fast and Robust Diffusion Posterior Sampling for MR Image Reconstruction Using the Preconditioned Unadjusted Langevin Algorithm.

Magnetic resonance in medicine·2026

Same author

FENCE: Flexible Electric Noise Reduction Endo-Shield for the Suppression of Electromagnetic Interference in Low-Field MRI.

NMR in biomedicine·2026

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: May 31, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Parallel imaging with nonlinear reconstruction using variational penalties.

Florian Knoll¹, Christian Clason, Kristian Bredies

¹Institute of Medical Engineering Graz University of Technology, Graz, Austria. florian.knoll@tugraz.at

Magnetic Resonance in Medicine

|June 29, 2011

Summary

This summary is machine-generated.

This study introduces a novel nonlinear inversion method for autocalibrated parallel imaging. The technique improves image reconstruction quality and noise suppression for arbitrary sampling patterns.

More Related Videos

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

Related Experiment Videos

Last Updated: May 31, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

Area of Science:

Medical Imaging
Image Reconstruction
Magnetic Resonance Imaging (MRI)

Background:

Parallel imaging techniques in MRI accelerate data acquisition but often suffer from artifacts and noise.
Existing reconstruction methods struggle with arbitrary sampling patterns and require accurate coil sensitivity information.

Purpose of the Study:

To develop an advanced nonlinear inversion method for autocalibrated parallel MRI.
To enhance image reconstruction quality and noise suppression using variational penalties.
To improve artifact removal for pseudorandom and radial sampling patterns.

Main Methods:

Nonlinear inversion approach utilizing an iteratively regularized Gauss-Newton method.
Joint estimation of image and coil sensitivities.
Incorporation of total variation (TV) and total generalized variation (TGV) regularization.

Main Results:

Achieved improved reconstruction quality through joint estimation of image and coil sensitivities.
Demonstrated superior noise suppression using TV and TGV regularization.
Showcased enhanced removal of sampling artifacts for pseudorandom and radial sampling patterns.
Validated the method with phantom and in vivo measurements.

Conclusions:

The proposed nonlinear inversion method offers robust autocalibration for parallel MRI.
This approach significantly enhances image quality and artifact reduction across various sampling schemes.
The technique holds promise for accelerating MRI scans without compromising diagnostic accuracy.