论文标题
用于动态对比增强CT灌注研究的剥离方法，使用三维深图像先验作为同时的空间和时间正常器
Denoising method for dynamic contrast-enhanced CT perfusion studies using three-dimensional deep image prior as a simultaneous spatial and temporal regularizer
论文作者
论文摘要
这项研究旨在提出一种使用三维深图像先验（DIP）的动态对比增强计算机断层扫描（DCE-CT）灌注研究的方法，并通过模拟研究研究了其具有不同正则化参数（ALPHA）值的总变化（TV）方法。在提出的浸入方法中，将DIP纳入了图像DeNo的约束优化问题中，作为同时的空间和时间正常化程序，使用乘数的交替方向方法求解。在仿真研究中，使用数字脑幻像生成DCE-CT图像，并使用具有不同暴露的X射线暴露噪声模型（15、30、50、75和100 MAS）来改变其噪声水平。脑血流（CBF）图像是从原始对比度增强（CE）图像中产生的，以及使用块循环奇异值分解获得的DIP和TV方法获得的图像。使用峰值信噪比（PSNR）和结构相似性指数（SSIM）评估CE图像的质量。为了比较通过不同方法获得的CBF图像以及从地面真相图像产生的方法，进行了线性回归分析。当使用DIP方法时，PSNR和SSIM并未显着取决于暴露，而SSIM是所有暴露的最高接触。使用电视方法时，它们显着取决于暴露和α值。线性回归分析的结果表明，通过DIP方法获得的CBF图像的线性性优于从原始CE图像和电视方法获得的CBF图像。我们的初步结果表明，DIP方法可用于将DCE-CT图像降低到低暴露，并提高其产生的CBF图像的准确性。
This study aimed to propose a denoising method for dynamic contrast-enhanced computed tomography (DCE-CT) perfusion studies using a three-dimensional deep image prior (DIP), and to investigate its usefulness in comparison with total variation (TV)-based methods with different regularization parameter (alpha) values through simulation studies. In the proposed DIP method, the DIP was incorporated into the constrained optimization problem for image denoising as a simultaneous spatial and temporal regularizer, which was solved using the alternating direction method of multipliers. In the simulation studies, DCE-CT images were generated using a digital brain phantom and their noise level was varied using the X-ray exposure noise model with different exposures (15, 30, 50, 75, and 100 mAs). Cerebral blood flow (CBF) images were generated from the original contrast enhancement (CE) images and those obtained by the DIP and TV methods using block-circulant singular value decomposition. The quality of the CE images was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). To compare the CBF images obtained by the different methods and those generated from the ground truth images, linear regression analysis was performed. When using the DIP method, the PSNR and SSIM were not significantly dependent on the exposure, and the SSIM was the highest for all exposures. When using the TV methods, they were significantly dependent on the exposure and alpha values. The results of the linear regression analysis suggested that the linearity of the CBF images obtained by the DIP method was superior to those obtained from the original CE images and by the TV methods. Our preliminary results suggest that the DIP method is useful for denoising DCE-CT images at ultra-low to low exposures and for improving the accuracy of the CBF images generated from them.
