Material decomposition of spectral CT images via attention-based global convolutional generative adversarial network

Spectral computed tomography(CT)based on photon counting detectors can resolve the energy of every single photon interacting with the sensor layer and be used to analyze material attenuation information under different energy ranges,which can be helpful for material decomposition studies.However,there is a considerable amount of inherent quantum noise in narrow energy bins,resulting in a low signal-to-noise ratio,which can consequently affect the material decomposition performance in the image domain.Deep learning technology is currently widely used in medical image segmentation,denoising,and recognition.In order to improve the results of material decomposition,we propose an attention-based global convolutional generative adversarial network(AGC-GAN)to decompose different materials for spectral CT.Specifically,our network is a global convolutional neural network based on an attention mechanism that is combined with a generative adversarial network.The global convolutional network based on the attention mechanism is used as the generator,and a patchGAN discriminant network is used as the discriminator.Meanwhile,a clinical spectral CT image dataset is used to verify the feasibility of our proposed approach.Extensive experimental results demonstrate that AGC-GAN achieves a better material decomposition performance than vanilla U-Net,fully convolutional network,and fully convolutional denseNet.Remarkably,the mean intersection over union,structural similarity,mean precision,PAcc,and mean F1-score of our method reach up to 87.31%,94.83%,93.22%,97.39%,and 93.05%,respectively.

The characteristics of oasis urban expansion and drive mechanism analysis:a case study on Ganzhou District in Hexi Corridor,China

Ganzhou District is an oasis city in the Zhangye Municipality of Gansu Province, China. Based on multi-temporal TM and ETM satellite remote sensing data in 1985, 1996, 2000, and 2012, and by using corrected figures of land use status over the same periods, the spatial area of Ganzhou District since 1985 was extracted with statistical methods, and urban spatial expansion was measured by quantitative research methods. The characteristics of spatial expansion of Ganzhou District were analyzed by urban expansion rate, expansion intensity index, compactness, fractal dimension, and the city center shift method. The results showed that the built-up area of Ganzhou District increased by 3.46 times during 1985-2012. The expansion in 1985 1996 was slow, during 1996 2000 it was rapid, and during 2000-2012 it was at a high speed. This city mainly expanded to the northeast and northwest. Government decision making had a decisive influence on urban expansion. Initially the expansion was uniform, but later the local tfansportation, economy, resources, population, and national policies factors had an obvious influence on urban expansion.