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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 6, 2025

Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving SSLE of Glass Crystals

Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving SSLE of Glass Crystals

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Data-Driven Volumetric Computed Tomography Image Generation From Surface Structures Using a Patient-Specific Deep

Shaoyan Pan¹, Chih-Wei Chang², Zhen Tian³

¹Departments of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia; Departments of Biomedical Informatics, Emory University, Atlanta, Georgia.

International Journal of Radiation Oncology, Biology, Physics

|November 22, 2024

Summary

This summary is machine-generated.

This study developed a novel framework to reconstruct 3D CT images from surface scans for lung cancer radiation therapy. This approach enhances tumor tracking accuracy by overcoming surface-guided radiation therapy limitations.

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

Medical Imaging
Radiation Oncology
Artificial Intelligence

Background:

Optical surface imaging offers radiation-dose-free monitoring in image-guided radiation therapy.
Limitations exist in correlating surface motion with internal tumor motion, impacting purely surface-guided radiation therapy (SGRT) accuracy.
Accurate intrafractional monitoring is crucial for effective lung cancer radiation therapy.

Purpose of the Study:

To develop a data-driven framework for reconstructing volumetric CT images from surface images.
To mitigate the limitations of SGRT in lung cancer radiation therapy.
To enable accurate tumor tracking during radiation delivery without additional radiation dose.

Main Methods:

A retrospective analysis of 50 lung cancer patients with 4D CT scans was performed.
A surface-to-volume image synthesis framework using cycle-consistency generative adversarial networks was employed.
Patient-specific models were trained and validated using 9 out of 10 4DCT phases, with 1 phase reserved for testing.

Main Results:

The framework accurately reconstructed volumetric CT images from surface data.
Generated CT images showed a gross tumor volume center of mass difference of 1.72 ± 0.87 mm compared to ground truth.
High similarity was observed with a structural similarity index measure of 0.94 ± 0.02 and Dice score of 0.81 ± 0.07.

Conclusions:

The proposed approach offers a novel solution for SGRT limitations in lung cancer radiation therapy.
Real-time volumetric imaging during treatment delivery can potentially enable accurate tumor tracking.
This data-driven framework addresses motion management challenges without relying on first principles modeling.