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Related Concept Videos

Downsampling01:20

Downsampling

336
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...
336
Upsampling01:22

Upsampling

374
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
374

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On the down-sampling of diffusion MRI data along the angular dimension.

Nan-Kuei Chen1, Ryan P Bell2, Christina S Meade2

  • 1Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA.

Magnetic Resonance Imaging
|June 26, 2021
PubMed
Summary
This summary is machine-generated.

A new spatial uniformity index accurately assesses diffusion MRI data quality after down-sampling. This index is more reliable than FA fitting residuals for evaluating data harmonization and motion artifact correction in diffusion imaging.

Keywords:
Data harmonizationDiffusion MRIDown-samplingSpatial uniformity index

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Biomedical Engineering

Background:

  • Diffusion MRI requires uniformly distributed gradient directions for accurate quantification of diffusion properties like fractional anisotropy (FA).
  • Data down-sampling is sometimes necessary for excluding motion artifacts or harmonizing multi-protocol data.
  • Assessing the impact of down-sampling on diffusion property measurements is crucial.

Purpose of the Study:

  • To develop a numerical procedure for down-sampling diffusion MRI data.
  • To introduce a spatial uniformity index for evaluating down-sampling schemes.
  • To quantitatively assess the impact of down-sampling on FA accuracy and fitting residuals.

Main Methods:

  • Implemented a numerical procedure for down-sampling diffusion MRI data.
  • Developed a spatial uniformity index for diffusion directions.
  • Evaluated down-sampled human diffusion MRI data (64/60 to 30 directions) for FA accuracy, fitting residuals, and spatial uniformity.

Main Results:

  • The proposed spatial uniformity index correlates with FA errors in down-sampled diffusion MRI data.
  • FA fitting residuals, commonly used for quality assessment, do not correlate with FA errors or the spatial uniformity index.
  • This indicates the spatial uniformity index is a better predictor of down-sampling quality.

Conclusions:

  • The spatial uniformity index offers a more valuable quality assessment for down-sampled diffusion MRI data compared to FA fitting residuals.
  • The developed software procedure can guide data harmonization in multi-site studies.
  • It also aids in assessing the impact of motion artifact rejection on diffusion measure accuracy.