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KMID : 1038720210320040172
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Progress in Medical Physics
2021 Volume.32 No. 4 p.172 ~ p.178
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Synthetic Computed Tomography Generation while Preserving Metallic Markers for Three-Dimensional Intracavitary Radiotherapy: Preliminary Study
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Jin Hyeong-Min
Kang Seong-Hee
Kang Hyun-Cheol
Choi Chang-Heon
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Abstract
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Purpose: This study aimed to develop a deep learning architecture combining two task models to generate synthetic computed tomography (sCT) images from low-tesla magnetic resonance (MR) images to improve metallic marker visibility.
Methods: Twenty-three patients with cervical cancer treated with intracavitary radiotherapy (ICR) were retrospectively enrolled, and images were acquired using both a computed tomography (CT) scanner and a low-tesla MR machine. The CT images were aligned to the corresponding MR images using a deformable registration, and the metallic dummy source markers were delineated using threshold-based segmentation followed by manual modification. The deformed CT (dCT), MR, and segmentation mask pairs were used for training and testing. The sCT generation model has a cascaded three-dimensional (3D) U-Net-based architecture that converts MR images to CT images and segments the metallic marker. The performance of the model was evaluated with intensity-based comparison metrics.
Results: The proposed model with segmentation loss outperformed the 3D U-Net in terms of errors between the sCT and dCT. The structural similarity score difference was not significant.
Conclusions: Our study shows the two-task-based deep learning models for generating the sCT images using low-tesla MR images for 3D ICR. This approach will be useful to the MR-only workflow in high-dose-rate brachytherapy.
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KEYWORD
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Cervical cancer, Deep learning, Synthetic computed tomography, Magnetic resonanceonly radiotherapy, Intracavitary radiotherapy
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