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Computed Tomography

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...

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Related Experiment Video

Updated: Jun 27, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

4DCT image artifact detection using deep learning.

Joshua W Carrizales¹, Mattison J Flakus², Dallin Fairbourn³

¹Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA.

Medical Physics

|November 14, 2024

Summary

This summary is machine-generated.

A new deep learning algorithm effectively detects motion artifacts in four-dimensional computed tomography (4DCT) scans used for radiation therapy. This advanced method improves anatomical visualization and functional measurements by identifying various artifact types.

Keywords:

4DCT deep learning motion artifacts

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Last Updated: Jun 27, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Radiation Oncology
Artificial Intelligence

Background:

Four-dimensional computed tomography (4DCT) is crucial for radiation therapy planning.
Motion artifacts in 4DCT scans can obscure anatomical details and distort functional measurements.

Purpose of the Study:

To develop a deep learning algorithm for identifying motion artifacts in 4DCT images.
The algorithm is designed to detect multiple artifact types, including duplication, misalignment, truncation, and interpolation.

Main Methods:

A U-net convolutional neural network was trained and validated on over 23,000 4DCT slices.
Model performance was evaluated using receiver operating characteristic (ROC) and precision-recall curves against manual annotations.
Sensitivity was adjusted to match human observer levels for artifact detection.

Main Results:

The model achieved high performance with sensitivity (0.78), specificity (0.99), and precision (0.58).
Area under the ROC curve was 0.99, and precision-recall AUC was 0.73.
The model demonstrated 8% higher sensitivity than existing state-of-the-art methods.

Conclusions:

A versatile deep learning model was developed for detecting various artifacts in 4DCT images.
This single model handles multiple artifact types, unlike previous single-artifact-type models.
The algorithm enhances the reliability of 4DCT imaging in radiation therapy.