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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Infimal convolution of total generalized variation functionals for dynamic MRI.

Matthias Schloegl1, Martin Holler2, Andreas Schwarzl1

  • 1Institute of Medical Engineering, Graz University of Technology, Stremayrgasse 16, Graz, Austria; BioTechMed-Graz, Graz, Austria.

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Summary
This summary is machine-generated.

This study introduces infimal convolution of total generalized variation (ICTGV) for faster dynamic MRI reconstruction. This method enables significant undersampling, leading to higher resolutions and shorter scan times without compromising image quality.

Keywords:
CMRdynamic magnetic resonance imaginginfimal convolutionperfusion imagingtotal generalized variationvariational models

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Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Magnetic Resonance Imaging

Background:

  • Dynamic MRI applications require efficient image reconstruction techniques.
  • Current methods face limitations in balancing spatial and temporal resolution with scan time.
  • Developing novel regularization methods is crucial for accelerating dynamic MRI.

Purpose of the Study:

  • To accelerate dynamic MRI applications using infimal convolution of total generalized variation (ICTGV) for spatio-temporal regularization.
  • To apply ICTGV for the first time to reconstruct dynamic MRI data, including CINE and perfusion scans.
  • To investigate the influence of time-dependent morphology and temporal contrast changes on reconstruction.

Main Methods:

  • ICTGV is presented as a new image prior for dynamic MRI data, balancing spatial and temporal regularity.
  • Reconstruction from subsampled MR data is formulated as a convex optimization problem.
  • A duality-based non-smooth optimization algorithm is employed to obtain global solutions.

Main Results:

  • Reconstruction error remains low with acceleration factors up to 16 for CINE and dynamic contrast-enhanced MRI.
  • A GPU implementation reduces reconstruction time for a single dataset to under 4 minutes, meeting clinical demands.
  • ICTGV allows for substantial undersampling, enabling high spatial and temporal resolutions and coverage.

Conclusions:

  • ICTGV-based dynamic MRI reconstruction facilitates significant undersampling, leading to very high spatial/temporal resolutions and reduced scan times.
  • The method offers robust and flexible decomposition into components with varying temporal regularity.
  • This approach provides a new, robust method for dynamic MRI reconstruction with improved efficiency and flexibility.