Lu Yanjie, Zheng Nan, Ye Mingtao, Zhu Yihao, Zhang Guodao, Nazemi Ehsan, He Jie
Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
College of Pharmacy, Wenzhou Medical University, Wenzhou 325035, China.
Diagnostics (Basel). 2023 Jan 4;13(2):190. doi: 10.3390/diagnostics13020190.
The air kerma is a key parameter in medical diagnostic radiology. Radiologists use the air kerma parameter to evaluate organ doses and any associated patient hazards. The air kerma can be simply described as the deposited kinetic energy once a photon passes through the air, and it represents the intensity of the radiation beam. Due to the heel effect in the X-ray sources of medical imaging systems, the air kerma is not uniform within the X-ray beam's field of view. Additionally, the X-ray tube voltage can also affect this nonuniformity. In this investigation, an intelligent technique based on the radial basis function neural network (RBFNN) is presented to predict the air kerma at every point within the fields of view of the X-ray beams of medical diagnostic imaging systems based on discrete and limited measured data. First, a diagnostic imaging system was modeled with the help of the Monte Carlo N Particle X version (MCNPX) code. It should be noted that a tungsten target and beryllium window with a thickness of 1 mm (no extra filter was applied) were used for modeling the X-ray tube. Second, the air kerma was calculated at various discrete positions within the conical X-ray beam for tube voltages of 40 kV, 60 kV, 80 kV, 100 kV, 120 kV, and 140 kV (this range covers most medical X-ray imaging applications) to provide the adequate dataset for training the network. The X-ray tube voltage and location of each point at which the air kerma was calculated were used as the RBFNN inputs. The calculated air kerma was also assigned as the output. The trained RBFNN model was capable of estimating the air kerma at any random position within the X-ray beam's field of view for X-ray tube voltages within the range of medical diagnostic radiology (20-140 kV).
空气比释动能是医学诊断放射学中的一个关键参数。放射科医生使用空气比释动能参数来评估器官剂量以及任何相关的患者风险。空气比释动能可以简单描述为光子穿过空气时沉积的动能，它代表了辐射束的强度。由于医学成像系统的X射线源存在足跟效应，空气比释动能在X射线束的视野范围内并不均匀。此外，X射线管电压也会影响这种不均匀性。在本研究中，提出了一种基于径向基函数神经网络（RBFNN）的智能技术，用于根据离散且有限的测量数据预测医学诊断成像系统X射线束视野内各点的空气比释动能。首先，借助蒙特卡罗N粒子X版本（MCNPX）代码对诊断成像系统进行建模。需要注意的是，使用了钨靶和厚度为1毫米的铍窗（未施加额外滤过器）对X射线管进行建模。其次，针对40 kV、60 kV、80 kV、100 kV、120 kV和140 kV的管电压，在锥形X射线束内的不同离散位置计算空气比释动能（该范围涵盖了大多数医学X射线成像应用），以提供用于训练网络的充足数据集。计算空气比释动能时的X射线管电压和各点位置用作RBFNN的输入。计算得到的空气比释动能也被指定为输出。经过训练的RBFNN模型能够估计医学诊断放射学范围内（20 - 140 kV）X射线管电压下X射线束视野内任意随机位置的空气比释动能。
