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Related Concept Videos

Instrument Calibration01:12

Instrument Calibration

Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
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...
Glassware Calibration01:11

Glassware Calibration

Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
Volumetric flasks: Volumetric flasks are designed to prepare aqueous solutions of precise volumes accurately with a calibration line on the neck. To calibrate a volumetric flask, it is important to fill it with distilled...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...

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Related Experiment Videos

Adaptive self-calibrating iterative GRAPPA reconstruction.

Suhyung Park1, Jaeseok Park

  • 1Department of Brain and Cognitive Engineering, Biomedical Imaging and Engineering Laboratory, Korea University, Seoul, Republic of Korea.

Magnetic Resonance in Medicine
|October 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive method for parallel magnetic resonance imaging (pMRI) that improves calibration accuracy and reduces artifacts, even at high acceleration rates.

Related Experiment Videos

Area of Science:

  • Medical Imaging
  • Biophysics
  • Signal Processing

Background:

  • Parallel magnetic resonance imaging (pMRI) uses coil signals for faster imaging.
  • Accurate calibration is crucial but challenging due to noise and varying correlations.
  • Existing methods struggle with corrupted calibration data and spatial variations.

Purpose of the Study:

  • To develop an adaptive iterative pMRI method for robust self-calibration.
  • To address challenges of noise and spatially varying correlations in calibration.
  • To improve reconstruction accuracy and reduce artifacts in accelerated pMRI.

Main Methods:

  • Developed an adaptive iterative generalized auto-calibrating partially parallel acquisition (G શ્રેણી) method.
  • Integrated a Kalman filter for dynamic estimation of spatial correlations and updating calibration signals.
  • Incorporated noise statistics into Kalman filter models for coil-weighted denoising.

Main Results:

  • The proposed method achieves highly accurate calibration, even with noisy and spatially varying data.
  • Demonstrated significant reduction in artifacts and noise in numerical and in vivo studies.
  • Successfully reconstructed images at high acceleration factors with improved quality.

Conclusions:

  • The adaptive iterative G શ્રેણી method with dynamic self-calibration enhances pMRI accuracy.
  • The Kalman filter framework effectively handles noise and spatial variations for improved calibration.
  • This approach offers a robust solution for artifact and noise reduction in accelerated pMRI.