Research on Material Decomposition of Dual-energy CT Image Based on Iterative Residual Network
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摘要:
双能计算机断层成像技术(DECT)由于其材料分解能力,在高级成像应用中发挥着重要作用。图像域分解直接对CT图像进行线性矩阵反演,但分解后的材料图像会受到噪声和伪影的严重影响。虽然各种正则化方法被提出来解决这个问题,但它们仍然面临着两个挑战: 繁琐的参数调整和过度平滑导致的图像细节损失。为此,本文提出一种基于迭代残差网络的双能CT图像材料分解算法,直接求逆作为初始基图像,利用堆叠的双通道卷积神经网络替换迭代分解模型中的正则化项,构成深度迭代分解网络,该方法同时实现了材料分解和噪声抑制。实验结果表明,本文提出的迭代残差网络优于其他对比方法,能够在保持基图像边缘细节信息的同时有效抑制噪声和伪影。
Abstract:Dual energy computed tomography (DECT) plays an important role in the application of advanced imaging due to its material decomposition capability. Image domain decomposition can directly invert CT images through by linear matrix, but the decomposed material images will be seriously affected by noise and artifacts. Although various regularization methods have been proposed to solve this problem, they still face two challenges: tedious parameter adjustment and the loss of image details resulted from over-smoothing. Therefore, in this paper we proposes a dual energy CT image material decomposition algorithm based on iterative residual network. Direct inversion is used as the initial base image, and a stacking two-channel convolutional neural network is used to replace the regularization items in the iterative decomposition model to form a deep iterative decomposition network. This method can realize material decomposition and noise suppression simultaneously. Experimental results show that the iterative residual network proposed in this paper is superior to other comparison methods and can effectively suppress noise and artifacts while maintaining the edge details of the base image.
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Keywords:
- computed tomography /
- dual-energy CT /
- residual network /
- noise suppression
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1 不同算法的定量评估结果
Materia Index DIMD DECT-ST DECT-MULTRA IR-Net RMSE 0.0815 0.0500 0.0341 0.0169 water PSNR 21.7735 26.0188 29.3327 35.4356 SSIM 0.4201 0.9276 0.9112 0.9937 RMSE 0.0786 0.0554 0.0440 0.0150 bone PSNR 22.0949 25.1253 27.1373 36.4688 SSIM 0.3399 0.8148 0.8949 0.9995 表 1 不同算法的定量评估结果
Table 1 Quantitative evaluation results of different algorithms
Materia Index DIMD DECT-ST DECT-MULTRA IR-Net RMSE 0.0815 0.0500 0.0341 0.0169 water PSNR 21.7735 26.0188 29.3327 35.4356 SSIM 0.4201 0.9276 0.9112 0.9937 RMSE 0.0786 0.0554 0.0440 0.0150 bone PSNR 22.0949 25.1253 27.1373 36.4688 SSIM 0.3399 0.8148 0.8949 0.9995 -
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