Poster + Presentation + Paper
4 April 2022 Pediatric CT cranial radiation dose estimation using deep learning
Author Affiliations +
Conference Poster
Abstract
Patient specific dose estimation is traditionally calculated though Monte-Carlo methods but not performed during CT image acquisition due to long computation times. We propose the implementation of a NN to perform patient specific dose estimation that can be performed alongside the CT acquisition due to the reduction in computation time. This is achieved by first performing MC simulations and then training the NN to replicate these predictions. The NN shows promise with a high degree of total cranial dose accuracy between the predictions and ground truth with a standard deviation of less than ±0.5 mGy.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas Holmes, Roozbeh Tarighati Rasekhi, Sara Shirazi, Erica Riedesel, and Amir Pourmorteza "Pediatric CT cranial radiation dose estimation using deep learning", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 120312L (4 April 2022); https://doi.org/10.1117/12.2612581
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KEYWORDS
Monte Carlo methods

Tissues

Bone

X-ray computed tomography

Neural networks

Medicine

Photons

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