Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networks
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The conceptus dose during diagnostic imaging procedures for pregnant patients raises health concerns owing to the high radiosensitivity of the developing embryo/fetus. The aim of this work is to develop a methodology for automated construction of patient-specific computational phantoms based on actual patient CT images to enable accurate estimation of conceptus dose.
We developed a 3D deep convolutional network algorithm for automated segmentation of CT images to build realistic computational phantoms. The neural network architecture consists of analysis and synthesis paths with four resolution levels each, trained on manually labeled CT scans of six identified anatomical structures. Thirty-two CT exams were augmented to 128 datasets and randomly split into 80%/20% for training/testing. The absorbed doses for six segmented organs/tissues from abdominal CT scans were estimated using Monte Carlo calculations. The resulting radiation doses were then compared between the computational models generated using automated segmentation and manual segmentation, serving as reference.
The Dice similarity coefficient for identified internal organs between manual segmentation and automated segmentation results varies from 0.92 to 0.98 while the mean Hausdorff distance for the uterus is 16.1 mm. The mean absorbed dose for the uterus is 2.9 mGy whereas the mean organ dose differences between manual and automated segmentation techniques are 0.07%, − 0.45%, − 1.55%, − 0.48%, − 0.12%, and 0.28% for the kidney, liver, lung, skeleton, uterus, and total body, respectively.
The proposed methodology allows automated construction of realistic computational models that can be exploited to estimate patient-specific organ radiation doses from radiological imaging procedures.
• The conceptus dose during diagnostic radiology and nuclear medicine imaging procedures for pregnant patients raises health concerns owing to the high radiosensitivity of the developing embryo/fetus.
• The proposed methodology allows automated construction of realistic computational models that can be exploited to estimate patient-specific organ radiation doses from radiological imaging procedures.
• The dosimetric results can be used for the risk-benefit analysis of radiation hazards to conceptus from diagnostic imaging procedures, thus guiding the decision-making process.
KeywordsMultidetector-row computed tomography Radiologic phantoms Patient-specific computational modeling Radiation dosimetry
Adaptive moment estimation
Convolutional neural networks
Dice similarity coefficient
Geneva University Hospital
Positron emission tomography
Positron emission tomography/computed tomography
Positive predictive value
Rectified linear unit
This work was supported by the Swiss National Science Foundation under grant SNSF 320030_176052 and Qatar National Research Fund under grant NPRP10-0126-170263. No other potential conflicts of interest relevant to this article exist.
This study has received funding by the Swiss National Science Foundation.
Compliance with ethical standards
The scientific guarantor of this publication is Habib Zaidi.
Conflict of interest
The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was waived in this study.
Institutional Review Board approval was obtained.
• Performed at one institution
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