A quality-checked and physics-constrained deep learning method to estimate material basis images from single-kV contrast-enhanced chest CT scans.
A quality-checked and physics-constrained deep learning method to estimate material basis images from single-kV contrast-enhanced chest CT scans.
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DOI：
10.1002/mp.16352
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发表时间：
2023-06
期刊：
影响因子：
3.8
通讯作者：
Chen, Guang-Hong
中科院分区：
文献类型：
--
作者：
Li, Yinsheng;Tie, Xin;Li, Ke;Zhang, Ran;Qi, Zhihua;Budde, Adam;Grist, Thomas M.;Chen, Guang-Hong
单千伏CT成像是放射学实践中的主要成像方法之一。然而，它不能为临床诊断中的一些细微病变定征任务提供物质基础图像。开发一种质量检查和物理约束的深度学习(DL)方法，从单千伏CT数据中估计物质基础图像，而不求助于双能量CT采集方案。利用深度神经网络将单千伏CT图像分解为两幅物质基础图像。该网络的作用是生成具有与输入单千伏CT图像相同矩阵维度的模板特征的特征空间。然后，对这些模板图像特征进行组合，以生成具有不同组合系数组的所需物质基础图像，每个物质基础图像一个。用两个单独的KV进行双能量CT图像采集，以生成单千伏CT图像和相应的两个物质基础图像之间的配对训练数据。为了确保获得的两个物质基础图像与实际投影数据中编码的光谱信息一致，在端到端训练中加入了两个物理约束，即(1)表征数据采集中的束硬化的每个测量投影基准的有效能量，以及(2)扫描器的物理因素，如探测器和管的特性。整个体系结构在本文中被称为深色。在应用阶段，生成的物质基础图像被发送到深度质量检查(Deep-QC)网络，以评估估计图像的质量，并向用户报告像素级估计误差。这些模型是使用来自48个临床病例的5592个训练和验证对来开发的。另外还使用了来自另外13名患者的1526张CT图像，以评估深色度法估计的水和碘基础图像的定量准确性。对于深色估计的碘基图像，与双能CT的平均差值为−0.2 5 mg/m L，符合限度为[−0.75 m g/m L，+0.2 4 m g/m L]。对于深色估计的水基图像，与双能CT的平均差值为0.0g/mL，符合限度为[−0.01g/mL，0.01g/mL]。在整个测试队列中，深色图像和双能材料图像之间的中位数[25%，75%]均方根误差：水图像为14[12，16]mg/mL，碘图像为0.73[0.64，0.80]mg/mL。当估计的物质基础图像中存在重大误差时，Deep-QC可以捕获这些误差并提供像素级误差图，以告知用户DL结果是否可信。Deep-en-Chroma网络提供了一种新的途径，可以从单千伏CT数据和Deep-QC模块中估计临床相关的材料基础图像，以便在实践中告知最终用户DL材料基础图像的准确性。
Single-kV CT imaging is one of the primary imaging methods in radiology practices. However, it does not provide material basis images for some subtle lesion characterization tasks in clinical diagnosis. To develop a quality-checked and physics-constrained deep learning (DL) method to estimate material basis images from single-kV CT data without resorting to dual-energy CT acquisition schemes. Single-kV CT images are decomposed into two material basis images using a deep neural network. The role of this network is to generate a feature space with 64 template features with the same matrix dimensions of the input single-kV CT image. These 64 template image features are then combined to generate the desired material basis images with different sets of combination coefficients, one for each material basis image. Dual-energy CT image acquisitions with two separate kVs were curated to generate paired training data between a single-kV CT image and the corresponding two material basis images. To ensure the obtained two material basis images are consistent with the encoded spectral information in the actual projection data, two physics constraints, that is, (1) effective energy of each measured projection datum that characterizes the beam hardening in data acquisitions and (2) physical factors of scanners such as detector and tube characteristics, are incorporated into the end-to-end training. The entire architecture is referred to as Deep-En-Chroma in this paper. In the application stage, the generated material basis images are sent to a deep quality check (Deep-QC) network to assess the quality of estimated images and to report the pixel-wise estimation errors for users. The models were developed using 5592 training and validation pairs generated from 48 clinical cases. Additional 1526 CT images from another 13 patients were used to evaluate the quantitative accuracy of water and iodine basis images estimated by Deep-En-Chroma. For the iodine basis images estimated by Deep-En-Chroma, the mean difference with respect to dual-energy CT is −0.25 mg/mL, and the agreement limits are [−0.75 mg/mL, +0.24 mg/mL]. For the water basis images estimated by Deep-En-Chroma, the mean difference with respect to dual-energy CT is 0.0 g/mL, and the agreement limits are [−0.01 g/mL, 0.01 g/mL]. Across the test cohort, the median [25th, 75th percentiles] root mean square errors between the Deep-En-Chroma and dual-energy material images are 14 [12, 16] mg/mL for the water images and 0.73 [0.64, 0.80] mg/mL for the iodine images. When significant errors are present in the estimated material basis images, Deep-QC can capture these errors and provide pixel-wise error maps to inform users whether the DL results are trustworthy. The Deep-En-Chroma network provides a new pathway to estimating the clinically relevant material basis images from single-kV CT data and the Deep-QC module to inform end-users of the accuracy of the DL material basis images in practice.
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