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Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion

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This study presents a novel machine learning method for retrospective motion correction in brain MRI. The technique significantly reduces motion artifacts, improving image quality for clinical applications.

Keywords:
convolutional neural networksdeep learningimage reconstructionmachine learningmagnetic resonancemotion correction

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Neuroscience

Background:

  • Motion artifacts are a significant challenge in Magnetic Resonance Imaging (MRI), degrading image quality and potentially leading to misdiagnosis.
  • Existing retrospective motion correction methods often struggle with scalability and efficiency, particularly for complex motion patterns.

Purpose of the Study:

  • To introduce and validate a scalable retrospective motion correction technique for brain MRI.
  • To integrate a machine learning component into model-based motion minimization for improved artifact removal.

Main Methods:

  • A convolutional neural network (CNN) was trained to correct motion artifacts in 2D T2-weighted rapid acquisition with refocused echoes (RARE) images.
  • The CNN was integrated into a model-based data-consistency optimization framework to jointly estimate motion parameters and reconstruct the uncorrupted image.
  • A separable motion model enabled efficient intrashot (line-by-line) motion correction, enhancing scalability.

Main Results:

  • The combined CNN and model-based approach demonstrated improved search convergence and separability compared to CNN alone.
  • Significant reduction in image space root mean square error was observed in simulations.
  • Substantial reduction of motion artifacts was confirmed in in vivo motion experiments.

Conclusions:

  • The developed method offers a scalable and effective solution for retrospective motion correction in brain MRI.
  • The integration of CNNs with model-based optimization shows promise for enhancing clinical MRI by mitigating motion artifacts post-acquisition.