Energy spectrum CT can provide higher CT imaging resolution and thus play an important role in clinical diagnosis. X-ray Absorption Spectrum (XAS) instrument based on photon counting detector has great potentials in energy spectrum CT. The main problems of PCDs are “Stacking effect” and “charge sharing”, which can cause spectrum distortion and therefore depress imaging quality. It is quite complicated to correct spectral distortion through hardware improvements and physical simulation. Hence, we proposed a method for x-ray absorption spectrum data correction based on adversarial neural networks. The generator of our proposed can generate corrected spectrum after training. Spectrum data for correction in our experiments was collected by laboratory equipment and the ground-truth was obtained by simulation software. Real data and simulated data were feed to the network to train the generator and discriminator simultaneously. Results of our experiment illustrated that the well-trained network can effectively recover the spectrum from the distorted spectrum data. We also evaluate the imaging quality with the corrected spectrum data. It can be seen that the quality of CT reconstruction image can be significantly enhanced through corrected spectrum data. Our method of using adversarial neural networks to generate noise-free x-ray spectrum provides new ideas for the clinical application of energy spectrum CT.
Photon-Counting-Detector (PCD) has a broad application prospect in medical X-ray computed tomography (CT) and Xray (XR) imaging, which can improve contrast and spatial resolution, optimize spectral imaging, and use energy-dependent attenuation coefficient for the great potential of material composition identification. However, the measurement provided by the photon-counting-detector causes spectral distortion due to physical phenomena such as pulse pileup effect, charge sharing, K-escape and Compton scattering occurring in the detector. Since the calculation of the physical phenomenon that causes distortion is very complicated, this paper proposes a method of using the neural network for spectral correction based on Monte Carlo simulation, that is, using the Monte Carlo method to simulate the particle transport process to obtain undistorted spectrum as the label of the neural network, the spectrum is used as the input data of the neural network, and the relationship between the distortion spectrum and the corrected spectrum is learned by training the neural network. After the training is completed, using the test set for model evaluation, the standard error between the predicted result and the label was only 25.1601ppm. This method can effectively correct the spectral distortion problem of the photon-countingdetector, and can more accurately invert the X-ray spectral data.
X-ray Absorption Spectroscopy (XAS) was been applied for the material recognition in this paper. Twelve kinds of plastics were selected as specimens. Each specimen was tested for 100 times by different operators for data processing. Seventy sets of spectral data of each specimen were randomly selected as training set and the other 30 sets were selected as testing set. Training set was calculated with Principal Component Analysis (PCA) to get the first four Principal Components, which totally explain 99% of the original spectrum. The first four Principal Components were built plastic classification model respectively through K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) methods. The classification accuracy reached 89.22%-98.17%. Experimental results demonstrate that organics could be recognized by XAS. It shows that the X-ray absorption spectroscopy contains the potential of other organics recognition or even organisms.
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