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

Determination of Expected Frequency01:08

Determination of Expected Frequency

1.7K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
1.7K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

501
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....
501
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

2.1K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
2.1K
Aliasing01:18

Aliasing

942
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...
942
NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

1.8K
A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
1.8K
IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

1.5K
In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Correction to: Ultra-low-field brain MRI morphometry: Test-retest reliability and correspondence to high-field MRI.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Diffusion Tensor MRI and Spherical-Deconvolution-Based Tractography on an Ultra-Low Field Portable MRI System.

Human brain mapping·2026
Same author

MRI faraday cage performance during the lifetime of clinical MRI systems.

Magma (New York, N.Y.)·2026
Same author

Commentary: The MRI scanner room door is a latent safety issue.

Magma (New York, N.Y.)·2025
Same author

Quantitative T<sub>1</sub> and Effective Proton Density (PD*) mapping in children and adults at 7T from an MP2RAGE sequence optimised for uniform T<sub>1</sub>-weighted (UNI) and FLuid And White matter Suppression (FLAWS) contrasts.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Ultra-low-field brain MRI morphometry: Test-retest reliability and correspondence to high-field MRI.

Imaging neuroscience (Cambridge, Mass.)·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: Apr 25, 2026

Implementation of a Reference Interferometer for Nanodetection
16:11

Implementation of a Reference Interferometer for Nanodetection

Published on: April 26, 2014

8.8K

PRIMO: Precise radiofrequency inference from multiple observations.

Francesco Padormo1, Arian Beqiri1, Shaihan J Malik1

  • 1King's College London, Division of Imaging Sciences and Biomedical Engineering, The Rayne Institute, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London, UK, SE1 7EH.

Magnetic Resonance in Medicine
|August 14, 2014
PubMed
Summary
This summary is machine-generated.

Precise Radiofrequency Inference from Multiple Observations (PRIMO) calibrates MRI systems by extracting both transmit and receive fields simultaneously. This unified framework simplifies calibration for advanced parallel MRI systems.

More Related Videos

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

11.0K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

8.7K

Related Experiment Videos

Last Updated: Apr 25, 2026

Implementation of a Reference Interferometer for Nanodetection
16:11

Implementation of a Reference Interferometer for Nanodetection

Published on: April 26, 2014

8.8K
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

11.0K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

8.7K

Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Radiofrequency (RF) Engineering
  • Medical Imaging Physics

Background:

  • Current MRI calibration methods often treat transmit and receive radiofrequency (RF) calibration independently.
  • Existing techniques do not leverage transmit calibration data for receive calibration, leading to inefficiencies.
  • Advanced MRI systems utilize parallel transmit and receive capabilities, necessitating integrated calibration approaches.

Purpose of the Study:

  • To introduce Precise Radiofrequency Inference from Multiple Observations (PRIMO), a novel reconstruction framework.
  • To develop a unified method for calibrating MRI systems with parallel transmit and receive RF capabilities.
  • To extract both transmit and receive RF fields from transmit calibration data without prior assumptions.

Main Methods:

  • PRIMO utilizes transmit calibration data to simultaneously infer both transmit and receive RF fields.
  • The framework was validated through numerical simulations and comparison against a gold standard dataset.
  • In-vivo data acquired at 3 Tesla was used to demonstrate the method's practical application.

Main Results:

  • PRIMO accurately reconstructs RF fields, achieving less than 3% error under realistic noise conditions compared to the gold standard.
  • High-quality 3D transmit and receive maps were generated for an 8 transmit/8 receive channel system.
  • Full system calibration was accomplished in approximately two minutes.

Conclusions:

  • PRIMO establishes a unified framework for simultaneous estimation of all transmit and receive RF fields.
  • This integrated calibration approach is crucial for modern MRI systems with highly parallel RF architectures.
  • The method offers efficient and accurate calibration, enhancing the performance of advanced MRI scanners.