A transmission measurement-based spectrum estimation method incorporating X-ray tube voltage fluctuation

Pan, Zengxiang; Li, Xu; Lin, Guoqin; Qin, Peishan; Piao, Zun; Huang, Shuang; Wu, Wangjiang; Qi, Mengke; Zhou, Linghong; Xu, Yangsheng

doi:10.21037/qims-22-1055

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…Using several spectral coefficients to represent the x-ray spectrum is another indirect measurement-based strategy (Zhao et al 2014, Chang et al 2020, Pan et al 2023. The complexity of resolving the spectrum decreases along with the number of variables that must be determined.…”

Section: Introductionmentioning

confidence: 99%

An indirect estimation of x-ray spectrum via convolutional neural network and transmission measurement

Lv,

Xu,

Wang

et al. 2024

Phys. Med. Biol.

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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 80kVp, and 0.006 keV and 4.44% for 100kVp, 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.

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Section: Introductionmentioning

confidence: 99%