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CALIBRATIONLESS MRI RECONSTRUCTION WITH A PLUG-IN DENOISER.

Shen Zhao1, Lee C Potter1, Rizwan Ahmad2

  • 1Department of Electrical and Computer Engineering, The Ohio State University.

Proceedings. IEEE International Symposium on Biomedical Imaging
|February 25, 2022
PubMed
Summary

Calibrationless Magnetic Resonance Imaging (MRI) accelerates scans by reconstructing images from less data. This study shows a new framework (HICU) combined with a denoiser is feasible for faster, high-quality brain imaging.

Keywords:
Calibrationless MRIparallel imagingproximal gradient descentstructured low-rank matrix completion

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Biophysics

Background:

  • Magnetic Resonance Imaging (MRI) offers superior soft-tissue contrast without ionizing radiation.
  • Long MRI acquisition times limit clinical utility, driving research into accelerated reconstruction from under-sampled data.
  • Calibrationless MRI enhances acceleration and sampling flexibility.

Purpose of the Study:

  • To investigate the feasibility of a novel calibrationless MRI reconstruction framework.
  • To leverage non-linear machine learning priors for improved image reconstruction.
  • To demonstrate the effectiveness of the High-dimensional Fast Convolutional Framework (HICU) with a plug-in denoiser.

Main Methods:

  • Development and application of the High-dimensional Fast Convolutional Framework (HICU).
  • Integration of a plug-in denoiser with the HICU framework.
  • Reconstruction of 2D brain MRI data from highly under-sampled k-space data.

Main Results:

  • Demonstrated feasibility of the HICU framework with a plug-in denoiser for calibrationless MRI.
  • Successful reconstruction of 2D brain MRI from under-sampled data.
  • Potential for higher acceleration rates and flexible sampling patterns.

Conclusions:

  • The HICU framework paired with a denoiser shows promise for accelerated, calibrationless MRI.
  • This approach can improve the efficiency of MRI acquisition, particularly for brain imaging.
  • Further research may expand its application to other imaging modalities and anatomical regions.