Tivnan Matthew, Gang Grace, Zhang Ruoqiao, Noël Peter, Sulam Jeremias, Stayman J Webster
Department of Biomedical Engineering, Johns Hopkins University. Baltimore, MD, USA.
Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
Proc SPIE Int Soc Opt Eng. 2022 Jun;12304. doi: 10.1117/12.2647138. Epub 2022 Oct 17.
Quantitative estimation of multi-material density images is an important goal for Spectral CT imaging. However, material decomposition is a poorly-conditioned nonlinear inverse problem. Maximum-likelihood model-based material decomposition results in very noisy material density image estimates. One increasingly popular strategy for noise reduction is to apply deep neural networks for multi-material image formation. The most common loss function is mean squared error with respect to supervised target images such as ground truth or higher-dose cases. However, we believe that the mean-squared error loss function has several issues for multi-material image formation. In this work, we present a new loss function which includes multiple noise realizations with separate weights on covariance and bias for joint denoising of all material bases. By modulating these weights, it is possible to tune the image quality of neural network output images. To demonstrate our proposed approach, we conducted a simulation of a water/calcium/gadolinium spectral CT imaging scenario using a deep neural network for multi-material image denoising. Our results show that by changing the weights of our proposed loss function, it is possible to control the tradeoff between variance and bias for individual materials as well as the control over the bias coupling between materials.
多物质密度图像的定量估算是光谱CT成像的一个重要目标。然而，物质分解是一个病态的非线性逆问题。基于最大似然模型的物质分解会导致物质密度图像估计中出现大量噪声。一种越来越流行的降噪策略是将深度神经网络应用于多物质图像生成。最常见的损失函数是相对于监督目标图像（如真实情况或高剂量病例）的均方误差。然而，我们认为均方误差损失函数在多物质图像生成方面存在几个问题。在这项工作中，我们提出了一种新的损失函数，该函数包括多个噪声实现，对所有物质基的协方差和偏差具有单独的权重，用于联合去噪。通过调整这些权重，可以调整神经网络输出图像的质量。为了证明我们提出的方法，我们使用深度神经网络对水/钙/钆光谱CT成像场景进行了多物质图像去噪模拟。我们的结果表明，通过改变我们提出的损失函数的权重，可以控制单个物质的方差和偏差之间的权衡，以及对物质之间偏差耦合的控制。
