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Adaptive phase correction of diffusion-weighted images.

Marco Pizzolato1, Guillaume Gilbert2, Jean-Philippe Thiran3

  • 1Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

Neuroimage
|October 21, 2019
PubMed
Summary

Adaptive phase correction (APC) improves magnetic resonance imaging by refining phase estimation in diffusion-weighted images (DWIs). This novel method reduces image alterations and bias, enhancing diagnostic accuracy.

Keywords:
Diffusion MRIOriented laplacianPhase correctionPhase estimationRician noise

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Image Processing

Background:

  • Phase correction (PC) is crucial for real-valued Magnetic Resonance Imaging (MRI) data, particularly diffusion-weighted images (DWIs).
  • Standard PC methods rely on phase estimation, often using regularization, but suboptimal regularization can distort image contrast.
  • Low signal-to-noise ratio in DWIs leads to non-Gaussian noise and signal overestimation bias.

Purpose of the Study:

  • To introduce Adaptive Phase Correction (APC), a novel method for improved phase estimation in MRI.
  • To address the limitations of suboptimal regularization in conventional phase correction techniques.
  • To reduce contrast alterations and bias in real-valued diffusion-weighted images.

Main Methods:

  • Developed APC, a complex-valued image regularization technique that incorporates local MRI noise variance.
  • Applied APC to estimate phase by accounting for noise characteristics.
  • Validated APC on both synthetic and acquired MRI data.

Main Results:

  • APC demonstrated reduced alterations in real-valued phase-corrected images compared to standard methods.
  • The proposed APC method significantly reduced bias in diffusion indices.
  • APC requires minimal human input, facilitating integration into existing MRI workflows.

Conclusions:

  • Adaptive Phase Correction (APC) offers a robust solution for enhancing the quality of diffusion-weighted MRI.
  • APC minimizes image artifacts and bias, leading to more reliable diffusion measurements.
  • The method's parameter-free nature promotes its widespread adoption in clinical and research settings.