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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 3, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests.

Antonio-Javier Garcia-Sanchez¹, Enrique Garcia Angosto², Jose Luis Llor¹

¹Department of Information and Communication Technologies, Universidad Politécnica de Cartagena (UPCT), Campus Muralla del Mar, E-30202 Cartagena, Spain.

Sensors (Basel, Switzerland)

|November 27, 2019

Summary

This study introduces a new method to reduce radiation dose in computed axial tomography (CT) scans. It ensures diagnostic accuracy while minimizing cancer risks from radiation exposure.

Keywords:

computed axial tomography dose machine learning patients

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Area of Science:

Medical Physics
Radiology
Machine Learning

Background:

Computed axial tomography (CT) involves radiation exposure, increasing long-term cancer risk in patients.
Lowering radiation doses is desirable but may impair diagnostic accuracy for detecting lesions.
A balance is needed between radiation reduction and maintaining diagnostic quality in CT imaging.

Purpose of the Study:

To develop a novel methodology for predicting optimal radiation doses in CT scans.
To reduce unnecessary radiation exposure without compromising diagnostic capabilities.
To establish a precise radiation dose protocol for various CT examinations.

Main Methods:

Utilized a dataset of over 50,000 patient CT dose records, categorized by standardized protocols.
Applied data cleaning techniques to remove outliers and atypical dose information.
Generated regression curves using various Machine Learning algorithms to model dose-response relationships.

Main Results:

Identified and selected the most effective analytical technique for each specific CT protocol (e.g., skull, abdomen, thorax, pelvis).
Quantified precise radiation dose levels recommended for radiologists per CT test.
Demonstrated a method for dose optimization based on dosimetry parameters.

Conclusions:

The proposed methodology enables accurate radiation dose prediction for CT scans.
Achieved reduction in radiation dose is possible without sacrificing diagnostic performance.
This approach supports safer CT practices by optimizing radiation levels for individual patient protocols.