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1 April 2024 Deep learning-based dose estimation in cone-beam computed tomography
Jinwoo Kim, Ho Kyung Kim, Minkook Cho
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Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129253G (2024) https://doi.org/10.1117/12.3006162
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
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Abstract
There is an increasing call for radiation dose tracking from medical examinations and patient-specific dose management has become a great concern. Especially, since computed tomography (CT) can lead to a significant amount of patient dose, fast and accurate CT dose estimation has become an important issue. For real-time scan protocol optimization and patient-specific dose management in cone-beam CT (CBCT), we introduce a deep-learning approach that estimates the absorbed dose distributions from CT scan data. The deep convolutional neural network model based on U-Net architecture is trained to predict the absorbed dose distribution from CT images. The model is trained in 3 different strategies that utilize datasets in 2D, 2.5D (slice-based), and 3D (image-based) forms. The validation of the proposed method is performed by comparative analysis with the Monte Carlo (MC) simulations for typical dentoalveolar CBCT protocols which consider the anthropomorphic head phantoms as a patient. The proposed approach shows good agreement with the MC method while consuming a significantly lower computational cost. This study will be useful for the development of dental CBCT imaging techniques in terms of patient-specific dose management.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Jinwoo Kim, Ho Kyung Kim, and Minkook Cho "Deep learning-based dose estimation in cone-beam computed tomography", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129253G (1 April 2024); https://doi.org/10.1117/12.3006162
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KEYWORDS
Cone beam computed tomography
Computed tomography
Simulations
3D image processing
X-ray computed tomography
X-rays
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