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

Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
¹H NMR Signal Integration: Overview00:58

¹H NMR Signal Integration: Overview

The intensity of a signal, which can be represented by the area under the peak, depends on the number of protons contributing to that signal. The area under each peak is shown as a vertical line called an integral, with the integral value listed under it, as seen in the proton NMR spectrum of benzyl acetate. Each integral value is divided by the smallest integral value to obtain the ratio of the number of protons producing each signal. The ratio reveals the relative number of protons and not...

You might also read

Related Articles

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

Sort by
Same author

Quantitative matching of forensic evidence fragments using fracture surface topography and statistical learning.

Nature communications·2024
Same author

A practical model-based segmentation approach for improved activation detection in single-subject functional magnetic resonance imaging studies.

Human brain mapping·2023
Same author

Personalized synthetic MR imaging with deep learning enhancements.

Magnetic resonance in medicine·2022
Same author

Reduced-Rank Tensor-on-Tensor Regression and Tensor-Variate Analysis of Variance.

IEEE transactions on pattern analysis and machine intelligence·2022
Same author

Quantitative matching of forensic evidence fragments utilizing 3D microscopy analysis of fracture surface replicas.

Journal of forensic sciences·2022
Same author

Classification with the matrix-variate-<i>t</i> distribution.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2021

Related Experiment Video

Updated: Jun 22, 2026

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

Noise estimation in magnitude MR datasets.

Ranjan Maitra1, David Faden

  • 1Department of Statistics, Iowa State University, Ames, IA 50012, USA.

IEEE Transactions on Medical Imaging
|June 13, 2009
PubMed
Summary

This study introduces an automatic noise estimation method for magnitude magnetic resonance (MR) images. The novel approach accurately estimates noise parameters without needing extensive background data, improving MR image analysis.

Area of Science:

  • Medical Imaging
  • Signal Processing

Background:

  • Accurate noise parameter estimation is crucial for various magnetic resonance (MR) imaging applications.
  • Existing methods often require a significant proportion of background voxels, limiting their applicability.

Purpose of the Study:

  • To develop an automatic noise estimation method for magnitude MR images.
  • To overcome the limitation of requiring substantial background data for noise estimation.

Main Methods:

  • Modeling the observed signal magnitude as a mixture of Rice distributions with a common noise parameter.
  • Utilizing the expectation-maximization (EM) algorithm for parameter estimation.
  • Introducing a novel approach for estimating the number of mixture components.

Main Results:

More Related Videos

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
06:01

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Related Experiment Videos

Last Updated: Jun 22, 2026

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
06:01

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

  • The proposed methodology demonstrates high performance on simulated data and physical phantoms.
  • The method was successfully applied to four clinical MR datasets, validating its practical utility.

Conclusions:

  • The developed automatic noise estimation technique is effective and versatile.
  • This method offers a robust solution for noise parameter estimation in MR imaging, applicable across diverse datasets.