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Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networks

  • Tianwu Xie
  • Habib ZaidiEmail author
Physics
  • 106 Downloads

Abstract

Objectives

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.

Methods

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.

Results

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.

Conclusion

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.

Key Points

• 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.

Keywords

Multidetector-row computed tomography Radiologic phantoms Patient-specific computational modeling Radiation dosimetry 

Abbreviations

Adam

Adaptive moment estimation

CNNs

Convolutional neural networks

CT

Computed tomography

DSC

Dice similarity coefficient

HD

Hausdorff distance

HUG

Geneva University Hospital

IQ

Intelligence quotient

PET

Positron emission tomography

PET/CT

Positron emission tomography/computed tomography

PPV

Positive predictive value

ReLu

Rectified linear unit

Notes

Acknowledgements

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.

Funding

This study has received funding by the Swiss National Science Foundation.

Compliance with ethical standards

Guarantor

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.

Informed consent

Written informed consent was waived in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Prospective

• Experimental

• Performed at one institution

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Division of Nuclear Medicine and Molecular ImagingGeneva University HospitalGeneva 4Switzerland
  2. 2.Geneva University NeurocenterGeneva UniversityGenevaSwitzerland
  3. 3.Department of Nuclear Medicine and Molecular ImagingUniversity of Groningen, University Medical Center GroningenGroningenNetherlands
  4. 4.Department of Nuclear MedicineUniversity of Southern DenmarkOdenseDenmark

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