论文标题
双能计算机断层扫描成像从对比度增强的单能计算机断层扫描
Dual-energy Computed Tomography Imaging from Contrast-enhanced Single-energy Computed Tomography
论文作者
论文摘要
在标准的计算机断层扫描（CT）图像中，具有相同Hounsfield单元（HU）的像素可以对应于不同的材料，因此，区分和量化材料的挑战是挑战性的。双能CT（DECT）可以区分多种材料，但是DECT扫描仪并未像单能CT（SECT）扫描仪中广泛使用。在这里，我们目的是通过使用标准教派数据来执行DECT成像的深度学习方法。我们设计了一种占主导地位和差异学习机制，以从教派数据中生成DECT图像。使用受欢迎的DE应用程序的患者的图像研究了基于深度学习的DECT方法的性能：虚拟非对比度（VNC）成像和对比度量化。临床相关的指标用于定量评估。预测和原始高能CT图像之间的绝对HU差异分别为1.3 hu，1.6 hu，1.8 hu，1.8 hu和1.3 hu，在主动脉，肝，脊柱和胃上的ROI分别为1.3 hu。从原始和深度学习的DECT图像获得的碘图之间的主动脉碘定量差异小于1.0 \％，而后者的材料图像中的噪声水平已降低了7倍以上。这项研究表明，使用深度学习方法可以实现具有单个低能数据的高度精确的DECT成像。提出的方法使我们能够获得高质量的DECT图像，而无需支付常规基于硬件的DECT解决方案的开销，从而导致了新的光谱CT成像范式。
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 purpose a deep learning approach to perform DECT imaging by using standard SECT data. We designed a predenoising and difference learning mechanism to generate DECT images from SECT data. The performance of the deep learning-based DECT approach was studied using images from patients who received contrast-enhanced abdomen DECT scan with a popular DE application: virtual non-contrast (VNC) imaging and contrast quantification. Clinically relevant metrics were used for quantitative assessment. The absolute HU difference between the predicted and original high-energy CT images are 1.3 HU, 1.6 HU, 1.8 HU and 1.3 HU for the ROIs on aorta, liver, spine and stomach, respectively. The aorta iodine quantification difference between iodine maps obtained from the original and deep learning DECT images is smaller than 1.0\%, and the noise levels in the material images have been reduced by more than 7-folds for the latter. This study demonstrates that highly accurate DECT imaging with single low-energy data is achievable by using a deep learning approach. The proposed method allows us to obtain high-quality DECT images without paying the overhead of conventional hardware-based DECT solutions and thus leads to a new paradigm of spectral CT imaging.
