使用器官特异性深度学习将牙锥束计算机断层扫描中的剂量面积乘积转换为有效剂量。

IF 2.9 2区 医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE
Ruben Pauwels
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引用次数: 0

摘要

目的:建立一种基于深度学习的牙锥束计算机断层扫描(CBCT)中剂量面积积(DAP)与患者剂量的精确转换方法。方法:采用PCXMC 2.0模拟成人幻影24384次CBCT曝光,采用管电压、滤波、源-等心距离、波束宽度/高度和等心位置排列。记录等效器官剂量和DAP值。接下来,使用上述扫描参数作为输入,使用Keras训练神经网络(NN)来估计每个器官每个DAP的等效剂量。探索了两种位置输入特征的方法:(1)“坐标”模式,使用等中心的(连续的)xyz坐标,以及(2)“AP/JAW”模式,使用(分类的)正位和颅侧位。每个网络都使用3/1/1数据分割进行训练、验证和测试。使用ICRP 103组织加权因子从神经网络输出的组合中计算有效剂量(ED)。将所得到的神经网络模型用于估计ED/DAP的性能与多元线性回归(MLR)模型以及直接转换系数(CC)模型进行了比较。结果:器官剂量/DAP的平均绝对误差(MAE)在“坐标”模式下为0.18%(骨表面)~ 2.90%(食道),在“AP/JAW”模式下为2.74%(红骨髓)~ 14.13%(脑)。两种模式对ED的MAE分别为0.23%和4.30%,MLR模型为5.70%,cc模型为20.19%-32.67%。结论:神经网络可以在牙科CBCT中基于DAP准确估计患者剂量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Converting dose-area product to effective dose in dental cone-beam computed tomography using organ-specific deep learning.

Objective: To develop an accurate method for converting dose-area product (DAP) to patient dose for dental cone-beam computed tomography (CBCT) using deep learning.

Methods: A total of 24 384 CBCT exposures of an adult phantom were simulated with PCXMC 2.0, using permutations of tube voltage, filtration, source-isocenter distance, beam width/height, and isocenter position. Equivalent organ doses as well as DAP values were recorded. Next, using the aforementioned scan parameters as inputs, neural networks (NN) were trained using Keras for estimating the equivalent dose per DAP for each organ. Two methods were explored for positional input features: (1) "Coordinate" mode, which uses the (continuous) XYZ coordinates of the isocentre, and (2) "AP/JAW" mode, which uses the (categorical) anteroposterior and craniocaudal position. Each network was trained, validated, and tested using a 3/1/1 data split. Effective dose (ED) was calculated from the combination of NN outputs using ICRP 103 tissue weighting factors. The performance of the resulting NN models for estimating ED/DAP was compared with that of a multiple linear regression (MLR) model as well as direct conversion coefficients (CC).

Results: The mean absolute error (MAE) for organ dose/DAP on the test data ranged from 0.18% (bone surface) to 2.90% (oesophagus) in "Coordinate" mode and from 2.74% (red bone marrow) to 14.13% (brain) in "AP/JAW" mode. The MAE for ED was 0.23% and 4.30%, respectively, for the two modes, vs. 5.70% for the MLR model and 20.19%-32.67% for the CCs.

Conclusions: NNs allow for an accurate estimation of patient dose based on DAP in dental CBCT.

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来源期刊
CiteScore
5.60
自引率
9.10%
发文量
65
审稿时长
4-8 weeks
期刊介绍: Dentomaxillofacial Radiology (DMFR) is the journal of the International Association of Dentomaxillofacial Radiology (IADMFR) and covers the closely related fields of oral radiology and head and neck imaging. Established in 1972, DMFR is a key resource keeping dentists, radiologists and clinicians and scientists with an interest in Head and Neck imaging abreast of important research and developments in oral and maxillofacial radiology. The DMFR editorial board features a panel of international experts including Editor-in-Chief Professor Ralf Schulze. Our editorial board provide their expertise and guidance in shaping the content and direction of the journal. Quick Facts: - 2015 Impact Factor - 1.919 - Receipt to first decision - average of 3 weeks - Acceptance to online publication - average of 3 weeks - Open access option - ISSN: 0250-832X - eISSN: 1476-542X
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