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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: Jan 2, 2026

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

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Published on: November 30, 2022

Deep Encoder-Decoder Adversarial Reconstruction(DEAR) Network for 3D CT from Few-View Data.

Huidong Xie¹, Hongming Shan¹, Ge Wang¹

¹Biomedical Imaging Center, Department of Biomedical Engineering, Center for Biotechnology &Interdisciplinary Studies, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY 12180, USA.

Bioengineering (Basel, Switzerland)

|December 15, 2019

Summary

This summary is machine-generated.

This study introduces a deep learning network for low-dose X-ray computed tomography (CT) imaging. The DEAR-3D network reconstructs high-quality 3D CT images from limited-view data, reducing radiation exposure.

Keywords:

deep encoder-decoder adversarial network (DEAR)deep learning few-view CT generative adversarial network (GAN)machine learning sparse-view CT

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

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

Published on: November 23, 2019

Area of Science:

Medical Imaging
Radiology
Artificial Intelligence

Background:

X-ray computed tomography (CT) is essential in clinical practice but involves ionizing radiation, increasing cancer risk.
Reducing radiation dose is a critical area of research in medical imaging.
Few-view CT image reconstruction offers a method to minimize radiation dose and enable stationary CT systems.

Purpose of the Study:

To develop and evaluate a novel deep learning network for 3D CT image reconstruction from few-view data.
To address the challenge of 3D artifacts in few-view reconstruction using a data-driven approach.
To improve the image quality of low-dose CT scans.

Main Methods:

A deep encoder-decoder adversarial reconstruction (DEAR) network, specifically DEAR-3D, was proposed.
The network was designed to directly reconstruct 3D volumes from 3D spiral cone-beam CT data.
The DEAR-3D network was validated on a public abdominal CT dataset.

Main Results:

The proposed DEAR-3D network demonstrated promising reconstruction results.
Compared to 2D deep learning methods, DEAR-3D effectively utilized 3D information for improved image quality.
The network showed potential for high-quality image reconstruction from limited-view data.

Conclusions:

The DEAR-3D network is a viable deep learning approach for few-view CT image reconstruction.
Utilizing 3D information significantly enhances reconstruction quality in low-dose CT.
This method holds promise for reducing radiation dose in clinical CT applications while maintaining diagnostic image quality.