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Prospective acceleration of diffusion tensor imaging with compressed sensing using adaptive dictionaries.

Darryl McClymont1, Irvin Teh1, Hannah J Whittington1

  • 1Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.

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Summary

Compressed sensing and fast imaging sequences significantly reduce cardiac diffusion MRI scan times. This method achieves high acceleration factors with minimal impact on image quality and tractography results.

Keywords:
adaptive dictionariescompressed sensingdiffusion MRIdiffusion tensor imagingheart structureprospective undersampling

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Area of Science:

  • Magnetic Resonance Imaging
  • Biomedical Engineering
  • Medical Physics

Background:

  • Diffusion MRI is crucial for cardiac imaging but suffers from long scan times due to multiple image acquisitions.
  • Reducing scan times is essential for improving patient comfort and enabling advanced diffusion MRI applications in cardiology.

Purpose of the Study:

  • To investigate the combination of compressed sensing and fast imaging sequences for significantly reducing acquisition times in cardiac diffusion MRI.
  • To develop and validate a novel k-space sampling strategy and compressed sensing reconstruction algorithm for accelerated cardiac diffusion MRI.

Main Methods:

  • Acquired fully sampled and prospectively undersampled diffusion tensor imaging data in rat hearts using a fast spin echo (FSE) sequence at acceleration factors of 2-6.
  • Reconstructed undersampled images using a compressed sensing framework with adaptive dictionaries for sparsity enforcement.
  • Performed tensor fitting and fiber tractography on reconstructed images.

Main Results:

  • Achieved acceleration factors up to six, with only modest increases in root mean square error for apparent diffusion coefficient (ADC) and fractional anisotropy (FA).
  • Mean ADC and FA values at an acceleration factor of six were within 2.5% and 5% of ground truth, respectively.
  • Observed marginal differences in fiber tractography results, indicating preserved structural information.

Conclusions:

  • Developed and validated a novel compressed sensing reconstruction algorithm and k-space sampling strategy for accelerated cardiac diffusion MRI.
  • Combined k-space undersampling (up to 6x) and FSE acquisition (up to 8x) dramatically reduced scan times compared to fully sampled spin echo imaging.
  • This approach offers a promising solution for faster and more efficient cardiac diffusion MRI acquisition.