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Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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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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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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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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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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Related Experiment Video

Updated: Oct 9, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Robust weakly supervised learning for COVID-19 recognition using multi-center CT images.

Qinghao Ye1,2, Yuan Gao3,4, Weiping Ding5

  • 1Hangzhou Ocean's Smart Boya Co., Ltd, China.

Applied Soft Computing
|December 22, 2021
PubMed
Summary
This summary is machine-generated.

A new AI model, CIFD-Net, effectively identifies COVID-19 on CT scans despite variations from different scanners. This automated tool aids radiologists by improving accuracy and efficiency in diagnosing coronavirus disease 2019.

Keywords:
COVID-19Medical image analysisMulti-domain shiftMulticenter data processingWeakly supervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • The COVID-19 pandemic necessitates rapid and accurate diagnosis, with CT scans being crucial for assessing infection severity.
  • Manual analysis of CT scans by radiologists is time-consuming and prone to fatigue-related errors, especially with increasing patient numbers.
  • Automated CT scan recognition tools are vital, but face challenges due to variations in image appearance caused by different scanner technologies (multi-domain shift).

Purpose of the Study:

  • To develop an automated 3D CT scan recognition model for COVID-19 detection.
  • To address and overcome the multi-domain shift problem in multi-center and multi-scanner CT imaging studies.
  • To improve the reliability, efficiency, and accuracy of COVID-19 diagnosis using CT scans.

Main Methods:

  • Proposed a novel COVID-19 CT scan recognition model named coronavirus information fusion and diagnosis network (CIFD-Net).
  • Employed a robust weakly supervised learning paradigm to handle variations in CT image appearance.
  • Designed the model to specifically tackle the multi-domain shift problem inherent in multi-center and multi-scanner data.

Main Results:

  • CIFD-Net demonstrated efficient handling of the multi-domain shift problem in COVID-19 CT scans.
  • The model reliably resolved issues related to differing appearances in CT scan images from various sources.
  • Achieved higher accuracy in COVID-19 detection compared to existing state-of-the-art methods.

Conclusions:

  • The proposed CIFD-Net model offers a dependable and efficient solution for automated COVID-19 diagnosis from CT scans.
  • The weakly supervised learning approach effectively mitigates the impact of multi-domain shifts, crucial for objective diagnosis.
  • This advancement supports reproducible and objective diagnosis and prognosis in the context of the ongoing pandemic.