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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: Oct 10, 2025

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

Abdominal computed tomography localizer image generation: A deep learning approach.

Zongxi Liu¹, Huimin Zhao¹, Xiang Fang¹

¹Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, 3202 N. Maryland Ave, Milwaukee, WI, 53201, USA.

Computer Methods and Programs in Biomedicine

|December 15, 2021

Summary

This summary is machine-generated.

Deep learning can reconstruct one Computed Tomography (CT) localization scan from another, reducing radiation dose. This method aids patient positioning and scan parameter definition in clinical CT exams.

Keywords:

CT localizer image Deep learning Encoder-decoder network Image generation Scaled mixture loss

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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

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Computed Tomography (CT) is a key clinical imaging tool but a major source of radiation exposure.
Standard CT protocols use two orthogonal localization scans for positioning and parameter setup, contributing to patient dose without diagnostic value.

Purpose of the Study:

To investigate the feasibility of using deep learning to reconstruct one CT localization scan from the other.
To reduce patient radiation dose and streamline clinical workflows by eliminating one localization scan.

Main Methods:

A modified encoder-decoder network with a scaled mixture loss function was developed.
12,487 clinical abdominal CT exams were used, randomly split into training, validation, and test sets (7:1:2 ratio).
Reconstructed images were evaluated against ground truth using location, profile, and attenuation prediction errors.

Main Results:

The model achieved average location errors of 1.02±3.37 mm (lateral) and 6.46±6.43 mm (AP).
Average profile errors were 4.43±2.02% (lateral) and 3.9±2.32% (AP).
Average attenuation errors were 6.2±2.94% (lateral) and 7.12±3.54% (AP).

Conclusions:

Reconstructed CT localization images, despite potential loss of internal organ detail, are effective for tube current modulation and patient positioning.
This deep learning approach can significantly reduce radiation dose and scan time in clinical CT examinations.