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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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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 III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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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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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.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
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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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

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Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
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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: Nov 12, 2025

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
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Accelerated multicontrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting

Kanghyun Ryu1,2, Jae-Hun Lee2, Yoonho Nam3

  • 1Department of Radiology, Stanford University, Stanford, CA, USA.

Medical Physics
|March 18, 2021
PubMed
Summary

This study combines joint parallel imaging (JPI) and joint deep learning (JDL) to accelerate synthetic magnetic resonance imaging (MRI) acquisition. The novel approach enhances image quality and reduces artifacts for faster, more efficient MRI scans.

Keywords:
deep learningmulticontrastparallel imagingsynthetic MRI

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Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging (MRI)
  • Artificial Intelligence in Healthcare

Background:

  • Synthetic MRI requires multicontrast images for quantitative parameter mapping (T1, T2, PD).
  • Current methods face limitations in acceleration and image quality for synthetic MRI.
  • Accelerated acquisition is crucial for improving patient comfort and throughput.

Purpose of the Study:

  • To develop and evaluate a combined joint parallel imaging (JPI) and joint deep learning (JDL) method for accelerated synthetic MRI.
  • To enhance the reconstruction of multicontrast images for quantitative parameter mapping.
  • To enable higher acceleration factors in synthetic MRI without compromising image quality.

Main Methods:

  • Extended and combined JPI and JDL for improved reconstruction.
  • JPI estimated missing k-space lines, followed by JDL for refinement using a modified VS-Net (JVS-Net).
  • Tested on multidynamic multiecho (MDME) images with acceleration factors of 4-8.

Main Results:

  • Achieved lower normalized root-mean-square error (nRMSE) and higher structural similarity index measure (SSIM) compared to individual JPI or JDL.
  • Demonstrated potential for artifact-free synthesized contrast-weighted images comparable to fully sampled acquisitions.
  • Successfully reconstructed highly accelerated synthetic MRI data.

Conclusions:

  • The combined JPI and JDL approach significantly enhances the reconstruction of highly accelerated synthetic MRIs.
  • This method offers a promising solution for faster and more efficient MRI examinations.
  • Future work may focus on further optimization and clinical validation.