Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, 71003, Heraklion, Crete, Greece.
Eur Radiol. 2022 Sep;32(9):6418-6426. doi: 10.1007/s00330-022-08756-w. Epub 2022 Apr 6.
To propose a machine learning-based methodology for the creation of radiation dose maps and the prediction of patient-specific organ/tissue doses associated with head CT examinations.
CT data were collected retrospectively for 343 patients who underwent standard head CT examinations. Patient-specific Monte Carlo (MC) simulations were performed to determine the radiation dose distribution to patients' organs/tissues. The collected CT images and the MC-produced dose maps were processed and used for the training of the deep neural network (DNN) model. For the training and validation processes, data from 231 and 112 head CT examinations, respectively, were used. Furthermore, a software tool was developed to produce dose maps from head CT images using the trained DNN model and to automatically calculate the dose to the brain and cranial bones.
The mean (range) percentage differences between the doses predicted from the DNN model and those provided by MC simulations for the brain, eye lenses, and cranial bones were 4.5% (0-17.7%), 5.7% (0.2-19.0%), and 5.2% (0.1-18.9%), respectively. The graphical user interface of the software offers a user-friendly way for radiation dose/risk assessment. The implementation of the DNN allowed for a 97% reduction in the computational time needed for the dose estimations.
A novel methodology that allows users to develop a DNN model for patient-specific CT dose prediction was developed and implemented. The approach demonstrated herein allows accurate and fast radiation dose estimation for the brain, eye lenses, and cranial bones of patients who undergo head CT examinations and can be used in everyday clinical practice.
• The methodology presented herein allows fast and accurate radiation dose estimation for the brain, eye lenses, and cranial bones of patients who undergo head CT examinations and can be implemented in everyday clinical practice. • The scripts developed in the current study will allow users to train models for the acquisition protocols of their CT scanners, generate dose maps, estimate the doses to the brain and cranial bones, and estimate the lifetime attributable risk of radiation-induced brain cancer.
提出一种基于机器学习的方法，用于创建辐射剂量图，并预测与头部 CT 检查相关的患者特定器官/组织剂量。
回顾性收集了 343 例行标准头部 CT 检查的患者数据。对每个患者进行了蒙特卡罗（MC）模拟，以确定患者器官/组织的辐射剂量分布。对采集的 CT 图像和 MC 生成的剂量图进行处理，并用于训练深度神经网络（DNN）模型。在训练和验证过程中，分别使用了 231 次和 112 次头部 CT 检查的数据。此外，还开发了一种软件工具，用于使用训练后的 DNN 模型从头部 CT 图像生成剂量图，并自动计算大脑和颅骨的剂量。
DNN 模型预测的剂量与 MC 模拟结果之间的平均（范围）百分比差异分别为大脑、晶状体和颅骨的 4.5%（0-17.7%）、5.7%（0.2-19.0%）和 5.2%（0.1-18.9%）。该软件的图形用户界面为辐射剂量/风险评估提供了一种用户友好的方式。DNN 的实现使剂量估算所需的计算时间减少了 97%。
开发并实施了一种允许用户为患者特定 CT 剂量预测开发 DNN 模型的新方法。本文所介绍的方法可用于准确快速地估算行头部 CT 检查的患者的大脑、晶状体和颅骨的辐射剂量，并可在日常临床实践中使用。
• 本文提出的方法可用于快速准确地估算行头部 CT 检查的患者的大脑、晶状体和颅骨的辐射剂量，并可在日常临床实践中使用。• 当前研究中开发的脚本将允许用户为其 CT 扫描仪的采集协议训练模型，生成剂量图，估计大脑和颅骨的剂量，并估计因辐射引起的脑癌的终生归因风险。
