Jenkins Parker J B, Schmidt Taly Gilat
Marquette University and Medical College of Wisconsin, Department of Biomedical Engineering, Milwaukee, United States.
J Med Imaging (Bellingham). 2021 Jan;8(1):013502. doi: 10.1117/1.JMI.8.1.013502. Epub 2021 Jan 9.
We investigated the performance of a neural network (NN) material decomposition method under varying pileup conditions. Experiments were performed at tube current settings that provided count rates incident on the detector through air equal to 9%, 14%, 27%, 40%, and 54% of the maximum detector count rate. An NN was trained for each count-rate level using transmission measurements through known thicknesses of basis materials (PMMA and aluminum). The NN trained for each count-rate level was applied to x-ray transmission measurements through test materials and to CT data of a rod phantom. Material decomposition error was evaluated as the distance in basis material space between the estimated thicknesses and ground truth. There was no clear trend between count-rate level and material decomposition error for all test materials except neoprene. As an example result, Teflon error was 0.33 cm at the 9% count-rate level and 0.12 cm at the 54% count-rate level for the x-ray transmission experiments. Decomposition error increased with count-rate level for the neoprene test case, with 0.65-cm error at 9% count-rate level and 1.14-cm error at the 54% count-rate level. In the CT study, material decomposition error decreased with increasing incident count rate. For example, the material decomposition error for Teflon was 0.089, 0.066, 0.054 at count-rate levels of 14%, 27%, and 40%, respectively. Results demonstrate over a range of incident count-rate levels that an NN trained at a specific count-rate level can learn the relationship between photon-counting spectral measurements and basis material thicknesses.
我们研究了神经网络（NN）材料分解方法在不同堆积条件下的性能。实验在管电流设置下进行，这些设置使得通过空气入射到探测器上的计数率等于最大探测器计数率的9%、14%、27%、40%和54%。使用通过已知厚度的基础材料（聚甲基丙烯酸甲酯和铝）的透射测量，针对每个计数率水平训练一个神经网络。将针对每个计数率水平训练的神经网络应用于通过测试材料的X射线透射测量以及棒状体模的CT数据。材料分解误差被评估为基础材料空间中估计厚度与真实厚度之间的距离。除氯丁橡胶外，对于所有测试材料，计数率水平与材料分解误差之间没有明显趋势。作为一个示例结果，在X射线透射实验中，聚四氟乙烯在9%计数率水平下的误差为0.33厘米，在54%计数率水平下的误差为0.12厘米。对于氯丁橡胶测试案例，分解误差随计数率水平增加，在9%计数率水平下误差为0.65厘米，在54%计数率水平下误差为1.14厘米。在CT研究中，材料分解误差随入射计数率增加而减小。例如，聚四氟乙烯在14%、27%和40%计数率水平下的材料分解误差分别为0.089、0.066和0.054。结果表明，在一系列入射计数率水平范围内，在特定计数率水平下训练的神经网络可以学习光子计数光谱测量与基础材料厚度之间的关系。
