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Computed Tomography

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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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Related Experiment Video

Updated: Jun 27, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence

Eduardo Pontes Reis^1,2,3, Louis Blankemeier⁴, Juan Manuel Zambrano Chaves^5,6

¹Department of Radiology, Stanford University, Stanford, CA, USA. eduardo.reis@einstein.br.

European Radiology

|April 29, 2024

Summary

This summary is machine-generated.

An open-source artificial intelligence (AI) algorithm accurately identifies contrast phases in abdominal CT scans. This tool demonstrates strong performance in both internal and external validation, improving diagnostic capabilities.

Keywords:

Abdomen Artificial intelligence Contrast media Machine learning Radiomics

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Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

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Published on: March 24, 2023

Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Accurate contrast phase identification in abdominal CT scans is crucial for subsequent AI applications and biomarker quantification.
Existing methods for phase determination, such as Digital Imaging and Communications in Medicine (DICOM) labels, can be inaccurate.
There is a need for reliable and automated methods to detect contrast phases.

Purpose of the Study:

To develop and validate an open-source artificial intelligence (AI) algorithm for accurate detection of contrast phases in abdominal CT scans.
To create an interpretable AI model that can generalize across different datasets.

Main Methods:

A retrospective study developed an AI algorithm using 739 abdominal CT exams.
Deep learning-based segmentation (TotalSegmentator) and radiomics features were employed.
A gradient-boosting classifier was trained to identify four contrast phases: non-contrast, arterial, venous, and delayed.

Main Results:

The AI algorithm achieved 92.3% accuracy and 90.7% F1 score in internal validation.
External validation on the VinDr-Multiphase CT dataset showed 90.1% accuracy and 82.6% F1 score.
Shapley analysis identified renal and vascular radiodensity as key features for classification.

Conclusions:

The developed open-source AI algorithm accurately detects contrast phases in abdominal CT scans.
The algorithm demonstrates strong generalization capabilities through robust internal and external validation.
This AI tool can enhance downstream AI applications, biomarker quantification, and improve diagnostic accuracy and access.