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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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...
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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...
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and the...
X-ray Imaging01:24

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...

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Updated: May 19, 2026

Anatomical Reconstructions of the Human Cardiac Venous System using Contrast-computed Tomography of Perfusion-fixed Specimens
06:02

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Published on: April 18, 2013

Explainable Knowledge-Guided Algorithm for Contrast Extravasation Detection on Computed Tomography.

Tuan D Pham1, Maki Kitamura2, Taichiro Tsunoyama3

  • 1Barts and The London School of Medicine and DentistryQueen Mary University of London E1 2AD London U.K.

IEEE Journal of Translational Engineering in Health and Medicine
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a knowledge-guided AI framework for automated detection of contrast extravasation on CT scans. It significantly speeds up trauma triage by providing rapid, explainable alerts, aiding critical care decisions.

Keywords:
Traumaclinical knowledgecomputed tomographycontrast media extravasationknowledge-guided detection.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Contrast media extravasation is a critical finding in trauma CT.
  • Timely detection is essential for effective trauma triage and patient management.
  • Current manual review processes can be time-consuming in emergency settings.

Purpose of the Study:

  • To develop an explainable, knowledge-guided framework for automated contrast extravasation detection.
  • To evaluate its potential in accelerating time-critical trauma triage.
  • To maintain clinically acceptable sensitivity in detection.

Main Methods:

  • A mathematical framework encoding expert diagnostic rules was formulated.
  • Sequential CT slices were analyzed using a 2.5D approach integrating temporal and spatial information.
  • The model outputs a confidence score and binary alert, validated against surgeon assessment.

Main Results:

  • The framework achieved clinically acceptable sensitivity for contrast extravasation detection.
  • It substantially reduced the time-to-decision compared to manual review.
  • Positive cases were identified early in the CT volume, enabling rapid screening.

Conclusions:

  • Expert clinical reasoning can be translated into an interpretable computational model.
  • The knowledge-guided design allows for rapid, transparent, automated screening.
  • The framework shows promise as a decision-support tool for trauma triage acceleration.