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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: May 15, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
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DEEP IMAGE PRIOR WITH STRUCTURED SPARSITY (DISCUS) FOR DYNAMIC MRI RECONSTRUCTION.

Muhammad Ahmad Sultan1, Chong Chen1, Yingmin Liu1

  • 1The Ohio State University.

Proceedings. IEEE International Symposium on Biomedical Imaging
|May 12, 2025
PubMed
Summary

This study introduces DISCUS, a self-supervised deep learning method for dynamic MRI reconstruction. DISCUS effectively reconstructs images by discovering underlying temporal variations without needing to pre-specify manifold dimensionality.

Keywords:
Dynamic MRIdeep image prior (DIP)manifold learningunsupervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Image Reconstruction

Background:

  • High-quality training data is often unavailable for dynamic MRI.
  • Reconstructing dynamic images requires capturing temporal variations effectively.

Purpose of the Study:

  • To propose a self-supervised deep learning method, DISCUS, for dynamic MRI reconstruction.
  • To address the challenge of limited training data in dynamic MRI.
  • To discover the low-dimensional manifold of temporal variations without prior dimensionality specification.

Main Methods:

  • Developed DISCUS (Deep Image Prior with Structured Sparsity), a self-supervised deep learning approach.
  • DISCUS jointly optimizes network parameters and input code vectors.
  • Incorporates group sparsity on frame-specific code vectors to uncover temporal dynamics.

Main Results:

  • Validated DISCUS using numerical studies with dynamic phantoms and patient data.
  • DISCUS successfully discovered manifold dimensionality in simulated data.
  • Outperformed traditional Compressed Sensing (CS) and Deep Image Prior (DIP) in LGE phantom and patient data reconstruction.

Conclusions:

  • DISCUS offers a robust method for dynamic MRI reconstruction, especially with limited training data.
  • The structured sparsity approach in DISCUS enhances the discovery of true manifold dimensionality.
  • DISCUS provides significant performance gains over existing methods like CS and DIP.