Abstract：
Objective.In this work, we aim to propose an accurate and robust spectrum estimation method by synergistically combining x-ray imaging physics with a convolutional neural network (CNN).Approach.The approach relies on transmission measurements, and the estimated spectrum is formulated as a convolutional summation of a few model spectra generated using Monte Carlo simulation. The difference between the actual and estimated projections is utilized as the loss function to train the network. We contrasted this approach with the weighted sums of model spectra approach previously proposed. Comprehensive studies were performed to demonstrate the robustness and accuracy of the proposed approach in various scenarios.Main results.The results show the desirable accuracy of the CNN-based method for spectrum estimation. The ME and NRMSE were -0.021 keV and 3.04% for 80 kVp, and 0.006 keV and 4.44% for 100 kVp, superior to the previous approach. The robustness test and experimental study also demonstrated superior performances. The CNN-based approach yielded remarkably consistent results in phantoms with various material combinations, and the CNN-based approach was robust concerning spectrum generators and calibration phantoms.Significance. We proposed a method for estimating the real spectrum by integrating a deep learning model with real imaging physics. The results demonstrated that this method was accurate and robust in estimating the spectrum, and it is potentially helpful for broad x-ray imaging tasks.
摘要：
目的：在这项工作中，我们的目标是通过将X射线成像物理学与卷积神经网络（CNN）协同结合，提出一种准确且稳健的谱估计方法。
方法：该方法依赖于传输测量，并且估计的频谱被公式化为使用蒙特卡罗模拟生成的一些模型频谱的卷积求和。实际预测和估计预测之间的差异被用作训练网络的损失函数。我们将这种方法与先前提出的模型谱加权和方法进行了对比。进行了全面的研究，以证明所提出的方法在各种情况下的鲁棒性和准确性。＆＃xD;主要结果：结果表明，基于CNN的频谱估计方法具有理想的准确性。对于80kVp，ME和NRMSE分别为-0.021keV和3.04%，对于100kVp，0.006keV和4.44%，优于以前的方法。鲁棒性测试和实验研究也证明了优越的性能。基于CNN的方法在具有各种材料组合的幻像中产生了非常一致的结果，基于CNN的方法在频谱生成器和校准体模方面是稳健的。＆＃xD;意义：我们提出了一种通过将深度学习模型与真实成像物理集成来估计真实光谱的方法。结果表明，该方法在频谱估计方面具有准确性和鲁棒性。它可能有助于广泛的X射线成像任务。
