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Slab boundary artifact correction in multislab imaging using convolutional-neural-network-enabled inversion for slab

Jieying Zhang1, Simin Liu1, Erpeng Dai1,2

  • 1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China.

Magnetic Resonance in Medicine
|October 16, 2021
PubMed
Summary
This summary is machine-generated.

A new algorithm, CNN-enabled inversion for slab profile encoding (CPEN), effectively reduces slab boundary artifacts in multislab MRI. This method offers improved accuracy and faster computation compared to previous techniques.

Keywords:
3D multislabdeep learningmodel-based CNNsimultaneous multislabslab boundary artifacts

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Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging (MRI)

Background:

  • Slab boundary artifacts are a common issue in multislab MRI, affecting image quality.
  • These artifacts arise from the modulation of images by slab profiles and aliasing along the slice direction.

Purpose of the Study:

  • To propose a novel algorithm, CNN-enabled inversion for slab profile encoding (CPEN), for slab boundary artifact correction.
  • To address artifacts in both single-band multislab and simultaneous multislab (SMSlab) imaging.

Main Methods:

  • Developed an iterative algorithm based on convolutional neural networks (CNNs) to solve the inverse problem of artifact formation.
  • Trained the algorithm using diffusion-weighted SMSlab images from 7 healthy subjects.
  • Tested the algorithm on single-band multislab and SMSlab images from 5 healthy subjects at 1.3-mm and 1-mm isotropic resolutions.
  • Compared CPEN with a nonlinear inversion for slab profile encoding (NPEN) method.

Main Results:

  • CPEN successfully reduced slab boundary artifacts in both single-band multislab and SMSlab images.
  • The algorithm also suppressed artifacts in diffusion metric maps.
  • CPEN demonstrated fewer residual artifacts and superior quantitative assessment compared to NPEN.
  • Achieved a computational speedup of over 35 times compared to NPEN.

Conclusions:

  • CPEN effectively reduces slab boundary artifacts in multislab MRI acquisitions.
  • The proposed method exhibits enhanced artifact correction, robustness, and computational efficiency over NPEN.
  • CPEN holds potential for improving the accuracy of multislab imaging in high-resolution diffusion-weighted imaging (DWI) and functional MRI (fMRI).