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arXiv 1906.04874physics.med-ph
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通过深度学习,使用单能量CT数据的双能量CT成像是可行的

Dual-energy CT imaging using a single-energy CT data is feasible via deep learning

Wei Zhao, Tianling Lv, Peng Gao, Liyue Shen, Xianjin Dai, Kai Cheng, Mengyu Jia, Yang Chen, Lei Xing

摘要:

在标准的计算机断层成像(CT)图像中,具有相同亨氏单位(HU)的像素可以对应于不同的材料,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DECT扫描仪并不像单能CT(SECT)扫描仪那样广泛使用。在这里,我们开发了一种深度学习方法,通过使用标准SECT数据来执行DECT成像。开发了一种使用两阶段卷积神经网络(CNN)将低能量图像映射到高能量图像的深度学习模型。该模型使用接受对比增强腹部DECT扫描的患者进行评价,该扫描采用流行的DE应用程序:虚拟非对比(VNC)成像和对比度量化。对于脊柱、主动脉、肝脏和胃上的ROI,预测和原始高能CT图像之间的HU差异分别为3.47、2.95、2.38和2.40 HU。对于4个ROI,从原始DECT和深度学习DECT获得的VNC图像之间的HU差异分别为4.10、3.75、2.33和2.92 HU。从原始DECT和深度学习DECT图像获得的碘图之间的主动脉碘定量差异为0.9%,表明预测图像和原始高能CT图像之间具有高度一致性。这项研究表明,通过使用深度学习方法,可以使用单个低能量数据实现高度准确的DECT成像。该方法可以大大简化DECT系统的设计,降低扫描剂量和成像成本。

英文摘要:

In a standard computed tomography (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 DECT scanners are not widely available as single-energy CT (SECT) scanners. Here we develop a deep learning approach to perform DECT imaging by using standard SECT data. A deep learning model to map low-energy image to high-energy image using a two-stage convolutional neural network (CNN) is developed. The model was evaluated using patients who received contrast-enhanced abdomen DECT scan with a popular DE application: virtual non-contrast (VNC) imaging and contrast quantification. The HU differences between the predicted and original high-energy CT images are 3.47, 2.95, 2.38 and 2.40 HU for ROIs on the spine, aorta, liver, and stomach, respectively. The HU differences between VNC images obtained from original DECT and deep learning DECT are 4.10, 3.75, 2.33 and 2.92 HU for the 4 ROIs, respectively. The aorta iodine quantification difference between iodine maps obtained from original DECT and deep learning DECT images is 0.9\%, suggesting high consistency between the predicted and the original high-energy CT images. This study demonstrates that highly accurate DECT imaging with single low-energy data is achievable by using a deep learning approach. The proposed method can significantly simplify the DECT system design, reducing the scanning dose and imaging cost.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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