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Dynamic MRI using model-based deep learning and SToRM priors: MoDL-SToRM.

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This study introduces a novel framework for fast cardiac MRI reconstruction using deep learning and regularization, significantly reducing scan times while maintaining image quality. The method combines convolutional neural network (CNN) and manifold-based priors for efficient free-breathing, ungated data recovery.

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alternating minimizationfree breathing cardiac MRlearned priormodel-basednon-local priorsubject specific prior

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Image Reconstruction

Background:

  • Cardiac MRI (Magnetic Resonance Imaging) typically requires long acquisition times, limiting patient comfort and increasing motion artifacts.
  • Free-breathing and ungated protocols are desirable for patient tolerance but pose significant reconstruction challenges due to undersampled data.
  • Combining deep learning with traditional regularization methods offers a promising avenue for accelerated MRI.

Purpose of the Study:

  • To develop and validate a novel framework for reconstructing free-breathing and ungated cardiac MRI data.
  • To integrate deep-learned priors (CNN) with complementary image regularization penalties (SToRM) for enhanced reconstruction.
  • To achieve significant acceleration in cardiac MRI acquisition times without compromising image quality.

Main Methods:

  • Image reconstruction formulated as an optimization problem with data consistency, CNN denoising prior, and SmooThness regularization on manifolds (SToRM) prior.
  • An iterative algorithm alternating between CNN/SToRM denoising and conjugate gradients (CG) for data consistency minimization.
  • Unrolling the iterative algorithm into a deep network trained with exemplar data.

Main Results:

  • The proposed framework enables fast recovery of cardiac MRI data from <8.2s acquisition time per slice.
  • Reconstructions achieve image quality comparable to traditional methods requiring 42s acquisition time.
  • Demonstrates a fivefold reduction in cardiac MRI scan time.

Conclusions:

  • Combining deep-learned CNN priors with complementary regularization penalties (SToRM) is beneficial for cardiac MRI reconstruction.
  • The synergistic combination leverages both generalizable redundancies (CNN) and patient-specific information (SToRM).
  • The proposed framework effectively facilitates the integration of these complementary priors for accelerated and high-quality cardiac MRI.