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Photorealistic Learned Landscapes for Augmented Reality
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Cartesian SENSE and k-t SENSE reconstruction using commodity graphics hardware.

Michael S Hansen1, David Atkinson, Thomas S Sorensen

  • 1Centre for Cardiovascular MR, Institute of Child Health, University College London, London, UK. michael.hansen@ucl.ac.uk

Magnetic Resonance in Medicine
|February 29, 2008
PubMed
Summary

Modern graphics processing units (GPUs) accelerate SENSE reconstruction for faster MRI scans. This enables real-time, interactive imaging and parameter tuning, improving image quality and workflow.

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Area of Science:

  • Medical Imaging
  • Computational Science

Background:

  • SENSE (Sensitivity Encoding) reconstruction is crucial for accelerating MRI scans.
  • Traditional SENSE reconstruction can be computationally intensive, limiting real-time applications.

Purpose of the Study:

  • To investigate the use of commodity graphics processing units (GPUs) for accelerating SENSE reconstruction.
  • To enable real-time, interactive SENSE imaging and parameter adjustment.

Main Methods:

  • Implemented Cartesian SENSE and k-t SENSE reconstruction on modern GPUs.
  • Measured reconstruction times and compared them to acquisition times.

Main Results:

  • Achieved acceleration of SENSE inversion by up to two orders of magnitude.
  • Reconstruction times were significantly reduced, becoming faster than acquisition times.
  • Enabled real-time, interactive SENSE imaging, even with numerous receiver coils.

Conclusions:

  • GPU acceleration makes SENSE reconstruction highly efficient, removing it as a bottleneck.
  • This facilitates real-time, interactive MRI, enhancing user control over image reconstruction parameters.
  • The approach benefits both real-time and non-real-time imaging datasets.