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Related Concept Videos

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 7, 2026

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 for CT Synthesis in Radiotherapy.

Yike Guo¹, Yi Luo¹, Hamed Hooshangnejad^1,2

¹Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA.

Bioengineering (Basel, Switzerland)

|December 30, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) deep learning (DL) methods are revolutionizing radiation oncology by generating synthetic CT (sCT) images. This technology enhances image-guided adaptive radiotherapy and simulation-free workflows, improving patient care.

Keywords:

deep learning (DL)radiotherapy synthetic CT (sCT)

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Area of Science:

Radiotherapy
Medical Imaging
Artificial Intelligence

Background:

Deep learning (DL) methods are increasingly integrated into radiation oncology.
Synthetic Computed Tomography (sCT) image generation is a key area of interest.
sCT supports advanced clinical scenarios like image-guided adaptive radiotherapy (IGART) and simulation-free workflows.

Purpose of the Study:

To provide a comprehensive review of DL-based sCT synthesis in radiotherapy.
To discuss clinical applications across various imaging modalities.
To examine DL model architectures and training strategies.

Main Methods:

Review of recent studies on DL-based sCT synthesis.
Analysis of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs).
Examination of multi-modal imaging inputs (CBCT, MRI, diagnostic CT).

Main Results:

DL-based sCT synthesis shows significant potential across multiple clinical applications.
Various DL architectures, including CNNs and GANs, are effective for sCT generation.
Emerging training strategies are enhancing sCT quality and clinical applicability.

Conclusions:

AI-driven sCT generation can reduce imaging burden and improve dose accuracy in radiotherapy.
Accelerated workflow efficiency through sCT has the potential to significantly improve patient treatment outcomes.
Further research is needed to address challenges in clinical translation of DL algorithms.