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CALIBRATIONLESS PARALLEL MRI USING MODEL BASED DEEP LEARNING (C-MODL).

Aniket Pramanik1, Hemant Aggarwal1, Mathews Jacob1

  • 1The University of Iowa, Iowa City, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|February 15, 2021
PubMed
Summary
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We developed a fast deep learning method for calibrationless parallel MRI reconstruction. This new approach is significantly faster than existing methods and improves image quality without needing calibration data.

Area of Science:

  • Medical Imaging
  • Deep Learning
  • Magnetic Resonance Imaging

Background:

  • Calibrationless parallel MRI reconstruction is crucial for reducing scan times and artifacts.
  • Existing methods like structured low rank (SLR) and model-based deep learning (MoDL) have limitations in speed and calibration requirements.

Purpose of the Study:

  • To introduce a novel, fast deep learning approach for calibrationless parallel MRI reconstruction.
  • To enhance reconstruction speed and performance compared to existing methods.
  • To eliminate the need for calibration data, minimizing potential mismatches.

Main Methods:

  • A fast model-based deep learning approach generalizing structured low rank (SLR) methods.
  • Self-learning linear annihilation filters and pre-learning non-linear annihilation relations in the Fourier domain.
Keywords:
CNNParallel MRIcalibrationless

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  • Incorporation of a complementary spatial domain prior for hybrid regularization.

    • Main Results:

    • The proposed scheme is three orders of magnitude faster than traditional SLR methods.
    • Achieved improved performance over calibrated image domain MoDL approaches.
    • Successfully eliminated the need for fully sampled calibration regions.

    Conclusions:

    • The developed deep learning framework offers a significantly faster and more robust solution for calibrationless parallel MRI reconstruction.
    • The hybrid regularization scheme provides superior performance and flexibility.
    • This approach has the potential to accelerate MRI acquisition and improve diagnostic accuracy.