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Nonlinear multiscale regularisation in MR elastography: Towards fine feature mapping.

Eric Barnhill1, Lyam Hollis2, Ingolf Sack3

  • 1Clinical Research Imaging Centre, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, EH16 4TJ, UK.

Medical Image Analysis
|July 5, 2016
PubMed
Summary
This summary is machine-generated.

A new image processing pipeline, ESP, enhances Magnetic Resonance Elastography (MRE) by preserving fine details in viscoelastic parameter maps. This improved MRE analysis offers greater radiological insight.

Keywords:
Complex dualtree waveletDenoisingElastographyMagnetic resonance elastographyWave inversion

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Image Processing

Background:

  • In vivo elastography, particularly Magnetic Resonance Elastography (MRE), aims to map viscoelastic properties of tissues.
  • Current MRE post-processing techniques often struggle to preserve fine-scale information, limiting diagnostic potential.
  • There is a need for advanced image processing methods to extract more detailed information from MRE data.

Purpose of the Study:

  • To develop and validate a novel image processing pipeline, ESP (Elastography Software Pipeline), for MRE.
  • To create MRE maps of viscoelastic parameters (complex modulus magnitude |G*| and loss angle ϕ) that retain fine-scale information.
  • To improve the radiological information obtainable from in vivo elastography.

Main Methods:

  • Developed ESP pipeline incorporating wavelet-domain denoising, image-driven noise estimation, and feature detection.
  • Validated ESP using simulated data (FEM) and in vivo cohorts (brain, thigh, liver).
  • Quantified fine feature spectral energy using the Reduced Energy Ratio (RER) and compared ESP with MDEV and 2D-LFE.

Main Results:

  • ESP accurately estimated noise levels and detected local spatial frequencies at fine resolutions.
  • FEM inversions showed ESP's accuracy for |G*| and ϕ within 8% and 10% respectively, with some artefacts noted.
  • In vivo, ESP correlated well with MDEV (R=0.83) and liver stiffness estimates were within 7% of 2D-LFE.
  • ESP demonstrated a statistically significant increase in fine feature spectral energy for both |G*| and ϕ.

Conclusions:

  • ESP recovers finer frequency information in elastograms under typical conditions, though artefacts can affect accuracy.
  • The ESP pipeline enhances fine feature spectral energy in in vivo MRE compared to MDEV.
  • ESP shows improved performance with longer wavelengths, maintaining stability and robustness in MRE analysis.