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Related Concept Videos

Deconvolution01:20

Deconvolution

698
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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Reconstruction of Signal using Interpolation01:10

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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...
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Image reconstruction from phased-array data based on multichannel blind deconvolution.

Huajun She1, Rong-Rong Chen1, Dong Liang2

  • 1Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 84112.

Magnetic Resonance Imaging
|June 30, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new multichannel blind deconvolution (MBD) framework for magnetic resonance imaging (MRI) reconstruction. The method improves image uniformity without needing coil sensitivity information, outperforming existing techniques.

Keywords:
Image restorationMultichannel blind deconvolutionNon-uniform intensityPhased array coilsRegularization

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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Magnetic Resonance Imaging

Background:

  • Conventional MRI reconstruction methods like sum-of-squares can suffer from non-uniformity.
  • Reconstructing images without knowledge of coil sensitivities presents a significant challenge.

Purpose of the Study:

  • To develop a novel framework for magnetic resonance imaging (MRI) reconstruction that overcomes non-uniformity issues.
  • To jointly estimate image and coil sensitivity functions in the image domain without prior knowledge of sensitivities.

Main Methods:

  • A multichannel blind deconvolution (MBD) framework was developed for joint image and sensitivity function estimation.
  • Regularization techniques were employed to address the non-uniqueness inherent in MBD problems by exploiting function smoothness.

Main Results:

  • The proposed MBD algorithm demonstrated improved image uniformity compared to existing methods.
  • Successful reconstructions were achieved using simulation, phantom, and in vivo MRI data.

Conclusions:

  • The developed MBD framework provides a robust solution for uniform MRI image reconstruction without coil sensitivity information.
  • This approach offers a significant advancement in MRI image quality and reliability.