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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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Imaging Studies I: CT and MRI01:14

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Computed Tomography (CT) scan:
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Computed Tomography01:10

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Imaging Studies for Cardiovascular System IV: CMRI01:21

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Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Over-and-Under Complete Convolutional RNN for MRI Reconstruction.

Pengfei Guo1, Jeya Maria Jose Valanarasu2, Puyang Wang2

  • 1Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 18, 2021
PubMed
Summary

This study introduces an Over-and-Under Complete Convolutional Recurrent Neural Network (OUCR) for faster magnetic resonance (MR) image reconstruction. The OUCR method improves image quality from under-sampled data with fewer parameters.

Keywords:
Convolutional RNNDeep learningMRI reconstruction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Science

Background:

  • Magnetic resonance (MR) image reconstruction from under-sampled data is complex, often introducing artifacts.
  • Current deep learning methods use auto-encoders, focusing on global features which can be suboptimal for reconstruction.

Purpose of the Study:

  • To develop an improved deep learning model for MR image reconstruction.
  • To enhance the focus on local features while preserving global structures for better image quality.

Main Methods:

  • Proposed an Over-and-Under Complete Convolutional Recurrent Neural Network (OUCR).
  • OUCR combines an overcomplete branch (restrained receptive field for local features) and an undercomplete branch (global features).
  • Utilized Convolutional Recurrent Neural Networks (CRNNs) within the architecture.

Main Results:

  • The OUCR method demonstrated significant improvements over compressed sensing and existing deep learning techniques.
  • Achieved better MR image reconstruction from under-sampled data.
  • Required fewer trainable parameters compared to other methods.

Conclusions:

  • The OUCR architecture effectively balances local and global feature learning for superior MR image reconstruction.
  • OUCR offers a promising, parameter-efficient deep learning solution for under-sampled MR imaging challenges.