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Automated size-specific dose estimates framework in thoracic CT using convolutional neural network based on U-Net

Sakultala Ruenjit1,2,3, Punnarai Siricharoen4, Kitiwat Khamwan1,3,5

  • 1Medical Physics Program, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.

Journal of Applied Clinical Medical Physics
|January 31, 2024
PubMed
Summary

This study introduces an automated convolutional neural network (CNN) method for calculating size-specific dose estimates (SSDEs) in thoracic CT scans. The CNN accurately determines the corrected effective diameter (Deffcorr), offering a reliable alternative to manual calculations.

Keywords:
U-Net modelconvolutional neural networksize-specific dose estimatethoracic CT

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Area of Science:

  • Medical Imaging
  • Radiological Physics
  • Artificial Intelligence in Healthcare

Background:

  • Accurate radiation dose assessment is crucial in computed tomography (CT).
  • Size-specific dose estimates (SSDEs) help standardize dose reporting across different patient sizes.
  • Manual calculation of parameters like corrected effective diameter (Deffcorr) can be time-consuming and prone to variability.

Purpose of the Study:

  • To develop and validate an automated method for calculating SSDEs in thoracic CT using a convolutional neural network (CNN).
  • To determine SSDEs based on the corrected effective diameter (Deffcorr) derived from automated segmentation.
  • To compare the automated method's results with manual calculations and established dose metrics.

Main Methods:

  • A U-Net based CNN architecture was employed for automated segmentation of thoracic CT images.
  • The CNN segmented lung, bone, and other tissues to calculate dimensions and subsequently the corrected effective diameter (Deffcorr).
  • The developed model was trained on 108 thoracic CT datasets, and results were compared against manual measurements and water-equivalent diameter (Dw) calculations using linear regression and Bland-Altman analysis.

Main Results:

  • The automated CNN method demonstrated high agreement with manual calculations for SSDEs based on Deffcorr.
  • Mean SSDE values were comparable across methods: 14.3 ± 2.1 mGy (manual Deffcorr), 14.6 ± 2.2 mGy (Dw), and 14.5 ± 2.4 mGy (automated Deffcorr).
  • The trained U-Net model accurately predicted SSDEs, yielding results consistent with manual estimations.

Conclusions:

  • The proposed automated framework utilizing a CNN provides a reliable and efficient solution for calculating Deffcorr-based SSDEs in thoracic CT.
  • This AI-driven approach can streamline dose assessment in CT examinations.
  • The automated method offers a promising tool for improving the accuracy and consistency of radiation dose reporting in thoracic CT.