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Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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Integrated image reconstruction and gradient nonlinearity correction.

Shengzhen Tao1,2, Joshua D Trzasko2, Yunhong Shu2

  • 1Mayo Graduate School, Mayo Clinic, Rochester, Minnesota, USA.

Magnetic Resonance in Medicine
|October 10, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for magnetic resonance imaging (MRI) to correct gradient nonlinearity (GNL) during reconstruction, reducing spatial resolution loss compared to traditional post-reconstruction correction. This improves anatomical detail preservation in MRI scans.

Keywords:
gradient nonlinearitymodel-based reconstructionnonuniform fast Fourier transform

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Medical Physics
  • Image Reconstruction

Background:

  • Gradient nonlinearity (GNL) in MRI can cause geometric distortions and reduce image quality.
  • Conventional GNL correction is typically applied in the image domain after reconstruction.
  • This post-reconstruction correction can lead to significant spatial resolution loss, particularly in regions farther from the gradient isocenter.

Purpose of the Study:

  • To present a model-based reconstruction strategy for MRI that incorporates gradient nonlinearity (GNL) correction during the reconstruction process.
  • To demonstrate that this in-reconstruction GNL correction minimizes spatial resolution loss compared to conventional image-domain correction.
  • To evaluate the effectiveness of the proposed strategy on phantom and in vivo MRI data.

Main Methods:

  • A generic signal model for GNL-affected MRI acquisitions was developed.
  • The model was integrated into contemporary image reconstruction platforms.
  • Efficient nonuniform fast Fourier transform (NUFFT)-based computational routines were implemented for reconstruction.
  • The impact on spatial resolution was assessed using phantom data at various offsets and in vivo data (fully sampled and undersampled).

Main Results:

  • Phantom studies showed significantly less resolution loss with the proposed strategy compared to standard methods at distances >10 cm from isocenter (35 cm field-of-view gradient coil).
  • In vivo results indicated that the proposed strategy better preserves fine anatomical detail than retrospective GNL correction.
  • The proposed method achieved comparable geometric correction to the conventional approach.

Conclusions:

  • Correcting gradient nonlinearity (GNL) during the image reconstruction phase in MRI is more effective than post-reconstruction correction.
  • This in-reconstruction approach significantly reduces spatial resolution loss.
  • The strategy offers improved preservation of anatomical detail while maintaining accurate geometric correction.