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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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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Imaging Studies III: Computed Tomography01:27

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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.
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Positron Emission Tomography01:29

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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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
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Generation of Virtual Non-Contrast CT From Intravenous Enhanced CT in Radiotherapy Using Convolutional Neural

Gao Liugang1,2, Xie Kai1,2, Li Chunying1,2

  • 1Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou, China.

Frontiers in Oncology
|October 5, 2020
PubMed
Summary

Virtual non-contrast (VNC) CT images generated from enhanced CT scans using convolutional neural networks (CNN) accurately replicate real non-contrast CT for radiotherapy dose calculations. This method reduces uncertainty in treatment planning.

Keywords:
convolutional neural networksdoseenhanced CTradiotherapyvirtual non-contrast CT

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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Artificial Intelligence in Medicine

Background:

  • Enhanced computed tomography (CT) scans can introduce dose calculation uncertainties in radiotherapy planning.
  • Virtual non-contrast (VNC) CT imaging offers a potential solution to mitigate these uncertainties.

Purpose of the Study:

  • To develop a method for generating VNC CT images from enhanced CT scans using convolutional neural networks (CNNs).
  • To compare the accuracy of dose calculations between enhanced CT, VNC CT, and real non-contrast CT.
  • To evaluate the impact of VNC CT on radiotherapy treatment planning for esophageal cancer.

Main Methods:

  • Utilized a U-Net architecture CNN to learn the transformation from enhanced CT to VNC CT.
  • Trained and tested the model on 50 patients with paired non-contrast and enhanced CT scans.
  • Performed dose calculations for esophageal cancer radiotherapy plans using enhanced CT, VNC CT, and real non-contrast CT datasets.

Main Results:

  • VNC CT images demonstrated significantly lower CT value errors compared to enhanced CT when compared to real non-contrast CT (6.7 ± 1.3 HU vs. 32.3 ± 2.6 HU).
  • Organ CT values in VNC CT showed no significant difference from real non-contrast CT, unlike enhanced CT.
  • Dose calculations using VNC CT were virtually identical to those using real non-contrast CT, with improved gamma passing rates (0.996 vs. 0.973).

Conclusions:

  • Generating VNC CT from enhanced CT using U-Net architecture is a reliable method for accurate radiotherapy dose calculation.
  • This approach minimizes dose calculation uncertainties associated with enhanced CT scans.
  • VNC CT provides a viable alternative to real non-contrast CT for radiotherapy planning, ensuring treatment accuracy.