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Related Concept Videos

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

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
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Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks.

Maarten L Terpstra1,2, Matteo Maspero1,2, Joost J C Verhoeff1

  • 1Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.

Medical Physics
|August 1, 2023
PubMed
Summary

A new deep learning model, MODEST, reconstructs high-quality four-dimensional MRI (4D-MRI) rapidly for radiation therapy. This accelerates image acquisition and reconstruction, improving motion quantification for MRI-guided radiotherapy.

Keywords:
4D-MRIMR Linacmachine learningradiotherapyrespiratory motion

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiotherapy Physics

Background:

  • Respiratory-resolved 4D-MRI is crucial for accurate radiation treatment of mobile tumors.
  • Current 4D-MRI methods face challenges with long acquisition and reconstruction times.
  • Accelerated 4D-MRI is needed to improve the efficiency of MRI-guided radiotherapy (MRIgRT).

Purpose of the Study:

  • To develop a novel deep learning architecture for rapid, high-quality 4D-MRI acquisition and reconstruction.
  • To enable accurate motion quantification for MRI-guided radiotherapy (MRIgRT).
  • To reduce the time burden associated with 4D-MRI procedures.

Main Methods:

  • A novel convolutional neural network, MODEST, was designed for spatial and temporal decomposition of 4D-MRI data.
  • MODEST was trained on undersampled 4D-MRI data from 28 lung cancer patients.
  • The network reconstructs high-quality 4D-MRI, bypassing the need for computationally intensive 4D convolutions.

Main Results:

  • MODEST achieved superior image quality compared to a U-Net architecture, despite having 30 times fewer trainable parameters.
  • High-quality 4D-MRI reconstruction was achieved in approximately 2.5 minutes.
  • The method demonstrated high image quality and motion consistency.

Conclusions:

  • The MODEST network enables accelerated, high-quality 4D-MRI reconstruction.
  • This advancement is particularly beneficial for MRI-guided radiotherapy (MRIgRT).
  • MODEST offers a promising solution for improving the efficiency and accuracy of radiation treatments.