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High-definition motion-resolved MRI using 3D radial kooshball acquisition and deep learning spatial-temporal 4D

Victor Murray1, Can Wu1, Ricardo Otazo1,2

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This study introduces HD-Movienet, a deep learning method for fast, high-definition lung MRI. It enables motion-resolved 4D MRI with 1.1 mm resolution in under 5 minutes, improving clinical free-breathing imaging.

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Area of Science:

  • Magnetic Resonance Imaging
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Free-breathing lung MRI is crucial for high-definition imaging but challenged by motion artifacts.
  • Current methods often require long scan times or compromise resolution.

Purpose of the Study:

  • To develop a novel motion-resolved, high-definition (HD) 4D MRI technique for free-breathing lung imaging.
  • To achieve 1.1 mm isotropic resolution with scan times under 5 minutes using deep learning reconstruction.

Main Methods:

  • Utilized a 3D radial kooshball acquisition sequence with ultrashort echo times.
  • Developed two HD-Movienet deep learning models (2D and 3D convolutional kernels) for reconstructing motion-sorted data.
  • Applied motion-sorting via amplitude-binning on respiratory signals.

Main Results:

  • HD-Movienet achieved reconstruction times under 6 seconds, significantly faster than XD-GRASP (>10 minutes).
  • Maintained comparable image quality to XD-GRASP with reduced scan times (2-4 minutes).
  • 3D-based HD-Movienet enhanced reconstruction quality at the cost of slightly longer reconstruction times (<11 seconds).

Conclusions:

  • HD-Movienet enables feasible motion-resolved 4D lung MRI with 1.1 mm isotropic resolution.
  • Achieved scan times of 2 minutes (4 motion states) and 4 minutes (10 motion states).
  • Represents a significant advancement for clinical free-breathing high-definition lung MRI.