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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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Effective Dose Estimation in Computed Tomography by Machine Learning.

Matteo Ferrante¹, Paolo De Marco¹, Osvaldo Rampado²

¹Medical Physics Unit, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy.

Tomography (Ann Arbor, Mich.)

|January 24, 2025

Summary

This summary is machine-generated.

Machine learning models can accurately estimate effective dose (E) from CT scans using patient and scanner data, offering a faster alternative to traditional methods. This approach enhances radiological safety without requiring complex dose-tracking software.

Keywords:

artificial intelligence (AI)dose tracking patient radiation protection

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Area of Science:

Medical Imaging
Radiological Physics
Machine Learning in Healthcare

Background:

Computed tomography (CT) scans are essential diagnostic tools, but patient radiation exposure requires careful management.
Accurate estimation of effective dose (E) is crucial for ensuring patient safety in CT imaging.
Current methods for dose estimation can be complex or require specialized software.

Purpose of the Study:

To develop and evaluate machine learning models for predicting effective dose (E) from CT scans.
To estimate E using only patient and CT acquisition parameters, bypassing the need for dose-tracking software.
To compare the performance of machine learning algorithms against traditional dose estimation methods.

Main Methods:

Utilized a dataset of 69,037 CT acquisitions with dose-tracking software (DTS) for training and validation.
Trained and optimized various machine learning algorithms, including random forest, neural networks, and support vector machines.
Evaluated model performance using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared, including testing on an external dataset.

Main Results:

The random forest regressor achieved the best performance, with MAE of 0.416 mSv and MAPE of 7% on the test set.
Machine learning models significantly outperformed traditional methods like k-factors (MAE: 2.06 mSv) and multiple linear regression (MAE: 0.98 mSv).
The random forest model demonstrated strong generalization on an external dataset, yielding an MAE of 0.215 mSv and MAPE of 7.1%.

Conclusions:

Machine learning models can accurately estimate effective dose (E) in CT imaging using readily available patient and scanner parameters.
This approach provides a viable alternative to complex dose-tracking software for rapid and reliable dose assessment.
The findings support the integration of machine learning for improved radiological safety and dose management in clinical practice.