Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning
Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning
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DOI：
10.1142/9789811215636_0013
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发表时间：
2019-11
影响因子：
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通讯作者：
Wei Zhao;T. Lv;Rena Lee;Yang Chen;L. Xing
中科院分区：
文献类型：
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作者：
Wei Zhao;T. Lv;Rena Lee;Yang Chen;L. Xing
计算机断层扫描（CT）是一种基本的成像方式，用于生成活体内部解剖的横截面视图或询问物体的材料组成，它已被常规用于临床应用和无损检测。在标准的CT图像中，具有相同Hounsfield单位（HU）的像素可以对应不同的材料，因此区分和量化材料具有挑战性。双能CT （DECT）是区分多种材料的理想选择，但昂贵的DECT扫描仪并不像单能CT （SECT）扫描仪那样广泛使用。深度学习的最新进展提供了一种使能工具，可以结合先验知识在不同模式之间映射图像。在这里，我们开发了一种深度学习方法，通过使用标准的SECT数据来执行DECT成像。该方法的终点是一个能够为给定输入的低能CT图像提供高能CT图像的模型。使用对比增强的DECT图像验证了基于深度学习的DECT成像方法的可行性，并使用临床相关指标进行了评估。这项工作为许多具有标准SECT数据的DECT临床应用开辟了新的机会，并可能大大简化硬件设计，降低未来DECT系统的扫描剂量和图像成本。
Computed tomographic (CT) is a fundamental imaging modality to generate cross-sectional views of internal anatomy in a living subject or interrogate material composition of an object, and it has been routinely used in clinical applications and nondestructive testing. In a standard CT image, pixels having the same Hounsfield Units (HU) can correspond to different materials, and it is therefore challenging to differentiate and quantify materials. Dual-energy CT (DECT) is desirable to differentiate multiple materials, but the costly DECT scanners are not widely available as single-energy CT (SECT) scanners. Recent advancement in deep learning provides an enabling tool to map images between different modalities with incorporated prior knowledge. Here we develop a deep learning approach to perform DECT imaging by using the standard SECT data. The end point of the approach is a model capable of providing the high-energy CT image for a given input low-energy CT image. The feasibility of the deep learning-based DECT imaging method using a SECT data is demonstrated using contrast-enhanced DECT images and evaluated using clinical relevant indexes. This work opens new opportunities for numerous DECT clinical applications with a standard SECT data and may enable significantly simplified hardware design, scanning dose and image cost reduction for future DECT systems.