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Related Concept Videos

Upsampling01:22

Upsampling

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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...
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Related Experiment Video

Updated: May 5, 2026

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
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PyHySCO: GPU-enabled susceptibility artifact distortion correction in seconds.

Abigail Julian1, Lars Ruthotto1,2

  • 1Department of Computer Science, Emory University, Atlanta, GA, United States.

Frontiers in Neuroscience
|June 12, 2024
PubMed
Summary
This summary is machine-generated.

PyHySCO offers rapid, accurate correction of echo-planar imaging (EPI) distortions using reversed gradient polarity (RGP) methods. This new tool leverages GPUs for seconds-level 3D correction, outperforming existing methods.

Keywords:
GPU accelerationecho planar imagingparallelizationreversed gradient polaritysoftware

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Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Computational Neuroscience

Background:

  • Susceptibility artifacts in echo-planar imaging (EPI) are a significant challenge in MRI.
  • Existing reversed gradient polarity (RGP) correction methods are computationally intensive, taking minutes per volume.
  • Advances in hardware and algorithms necessitate updated correction tools.

Purpose of the Study:

  • To introduce PyTorch Hyperelastic Susceptibility Correction (PyHySCO), a fast and user-friendly tool for EPI distortion correction.
  • To enable 3D RGP correction in seconds using multi-threading and GPU acceleration.
  • To provide a reliable, training-free correction method based on established physical models.

Main Methods:

  • PyHySCO utilizes a physical distortion model and mathematical formulation for reliable correction.
  • An improved initialization scheme employs the 1D distortion correction method by Chang and Fitzpatrick.
  • The tool is implemented in PyTorch, supporting multi-threading and GPU utilization for speed.
  • Validation involved extensive numerical testing on 3T and 7T Human Connectome Project data.

Main Results:

  • PyHySCO achieves correction times of seconds per volume, a significant speed improvement.
  • The tool demonstrates accuracy comparable to leading RGP methods.
  • Validation confirmed the efficacy of the new initialization scheme and compared optimization algorithms.
  • Performance was tested across different hardware and arithmetic precisions.

Conclusions:

  • PyHySCO provides a fast, accurate, and reliable solution for EPI distortion correction.
  • The tool's efficiency and user-friendliness make it suitable for advanced neuroimaging research.
  • PyHySCO is freely available under the GNU public license, promoting accessibility.