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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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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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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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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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Multi-slice compressed sensing MRI reconstruction based on deep fusion connection network.

Peng Shangguan1, Wenjie Jiang1, Jiechao Wang1

  • 1Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.

Magnetic Resonance Imaging
|August 9, 2022
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Summary
This summary is machine-generated.

Deep fusion connection networks (DFCN) improve magnetic resonance imaging (MRI) reconstruction from undersampled data. This deep learning approach effectively reduces aliasing artifacts and enhances image quality by leveraging inter-slice correlations.

Keywords:
Compressed sensingDeep learningMagnetic resonance imagingMulti-slice reconstruction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Deep Learning

Background:

  • Magnetic Resonance Imaging (MRI) reconstruction from undersampled data is challenging due to aliasing artifacts.
  • Deep learning methods have shown promise but struggle with severe undersampling.

Purpose of the Study:

  • To develop a novel deep learning network for efficient MRI reconstruction.
  • To improve the de-aliasing and tissue structure restoration in highly undersampled MR images.

Main Methods:

  • A deep fusion connection network (DFCN) was designed to utilize correlation information between adjacent MRI slices.
  • The DFCN method was evaluated on the IXI and Calgary-Campinas-359 public datasets.

Main Results:

  • DFCN achieved superior reconstruction quality compared to state-of-the-art methods.
  • On T2-weighted IXI data (10x acceleration), PSNR reached 34.16 dB, SSIM 0.9626, and NMSE 0.1144.
  • On T1-weighted Calgary-Campinas-359 data (10x acceleration), PSNR reached 30.17 dB, SSIM 0.9259, and NMSE 0.1294.

Conclusions:

  • The proposed DFCN method effectively eliminates aliasing artifacts in undersampled MRI.
  • Leveraging inter-slice correlations significantly enhances the quality of reconstructed MR images.