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

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...
Methods of Obtaining Topography01:25

Methods of Obtaining Topography

Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
Cartesian Vector Notation01:28

Cartesian Vector Notation

Cartesian vector notation is a valuable tool in mechanical engineering for representing vectors in three-dimensional space, performing vector operations such as determining the gradient, divergence, and curl, and expressing physical quantities such as the displacement, velocity, acceleration, and force. By using Cartesian vector notation, engineers can more easily analyze and solve problems in various areas of mechanical engineering, including dynamics, kinematics, and fluid mechanics. This...
Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

You might also read

Related Articles

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

Sort by
Same author

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

Magnetic resonance in medicine·2026
Same author

OpenMRF: A Modular, Vendor-Neutral Open-Source Framework for Reproducible Magnetic Resonance Fingerprinting using Pulseq.

ArXiv·2026
Same author

Magnetic resonance fingerprinting of the bladder is feasible in men with lower urinary tract symptoms.

Abdominal radiology (New York)·2026
Same author

The Future of Cardiac Magnetic Resonance: Navigating Ultra-High and Low-Field Imaging (Part 1).

Magnetic resonance imaging clinics of North America·2026
Same author

The Future of Cardiac Magnetic Resonance: Navigating Ultra-High and Low-Field Imaging (Part 2).

Magnetic resonance imaging clinics of North America·2026
Same author

Characterization of Clinically Significant Prostate Cancer in the Peripheral Zone Using Rapid B<sub>1</sub>-Insensitive MR Fingerprinting.

Radiology·2026

Related Experiment Video

Updated: Jul 5, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Reconstruction of undersampled non-Cartesian data sets using pseudo-Cartesian GRAPPA in conjunction with GROG.

Nicole Seiberlich1, Felix Breuer, Robin Heidemann

  • 1Department of Experimental Physics 5, University of Würzburg, Am Hubland, Würzburg, Germany. neseiber@physik.uni-wuerzburg.de

Magnetic Resonance in Medicine
|April 23, 2008
PubMed
Summary

This study introduces a novel pseudo-Cartesian GRAPPA method for reconstructing nonaliased images from undersampled non-Cartesian k-space data. This technique enables efficient parallel imaging reconstruction with various non-Cartesian trajectories.

More Related Videos

A New Workflow for Sampling and Digitizing Increment Cores
07:05

A New Workflow for Sampling and Digitizing Increment Cores

Published on: September 27, 2024

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

Related Experiment Videos

Last Updated: Jul 5, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

A New Workflow for Sampling and Digitizing Increment Cores
07:05

A New Workflow for Sampling and Digitizing Increment Cores

Published on: September 27, 2024

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

Area of Science:

  • Magnetic Resonance Imaging
  • Image Reconstruction
  • Parallel Imaging

Background:

  • Cartesian sampling is standard for Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA).
  • Non-Cartesian sampling offers advantages like faster acquisition but poses reconstruction challenges.
  • Extending Cartesian GRAPPA to non-Cartesian data is complex due to irregular sampling.

Purpose of the Study:

  • To develop a novel method for reconstructing nonaliased images from undersampled non-Cartesian k-space data.
  • To adapt the Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) algorithm for non-Cartesian trajectories.
  • To demonstrate the flexibility and applicability of the proposed method across various non-Cartesian sampling schemes.

Main Methods:

  • Implemented a pseudo-Cartesian GRAPPA reconstruction technique.
  • Utilized GRAPPA Operator Gridding (GROG) for gridding non-Cartesian data.
  • Employed multiple Cartesian patterns for reconstruction of undersampled data.

Main Results:

  • Successfully reconstructed nonaliased images from undersampled non-Cartesian k-space data.
  • Demonstrated the method's effectiveness with radial, rosette, spiral, 1D non-Cartesian, and zig-zag trajectories.
  • Showcased the flexibility of using variable Cartesian patterns for reconstruction.

Conclusions:

  • The proposed pseudo-Cartesian GRAPPA method provides a viable solution for parallel imaging reconstruction with non-Cartesian data.
  • This approach overcomes the limitations of traditional GRAPPA with non-Cartesian sampling.
  • The technique is adaptable to diverse non-Cartesian trajectories, enhancing imaging flexibility.