使用标准单能量CT扫描仪进行虚拟单色光谱CT成像的深度学习方法
A deep learning approach for virtual monochromatic spectral CT imaging with a standard single energy CT scanner
摘要：
目的/目标：开发和评估使用深度学习（DL）从单能量CT（SECT）扫描生成虚拟单色CT（VMCT）图像的策略。材料/方法：所提出的数据驱动的VMCT成像包括两个步骤：（i）使用用大量100 kV和140 kV双能CT（DECT）图像对训练的监督DL模型，从低能图像产生相应的高能CT图像;以及（ii）重建能量范围为40至150 keV的VMCT图像。为了评估该方法的性能，我们回顾性研究了6,767张腹部DECT图像。将DL-DECT图像重建的VMCT图像与DECT扫描仪重建的VMCT图像进行定量比较。配对样本t检验用于统计分析，以显示计算的HU值的一致性和精密度。结果：发现DL-DECT和真实DECT图像之间具有极好的一致性（p值范围为0.50至0.95）。与使用标准DECT获得的噪声相比，基于DL的VMCT成像实现了高达68%的噪声降低（从163 HU到51 HU）。对于基于DL的VMCT，在40 keV时达到每例患者的最大碘对比度噪声比（CNR）（范围为15.1至16.6）。除了仅使用SECT图像的VMCT采集的巨大益处之外，所提出的方法还实现了高达55%的CNR（从10.7到16.6）的改善。结论：这项研究表明，高质量的VMCT图像可以获得只有一个SECT扫描。
英文摘要：
Purpose/Objectives: To develop and assess a strategy of using deep learning (DL) to generate virtual monochromatic CT (VMCT) images from a single-energy CT (SECT) scan. Materials/Methods: The proposed data-driven VMCT imaging consists of two steps: (i) using a supervised DL model trained with a large number of 100 kV and 140 kV dual-energy CT (DECT) image pairs to produce the corresponding high-energy CT image from a low-energy image; and (ii) reconstructing VMCT images with energy ranging from 40 to 150 keV. To evaluate the performance of the method, we retrospectively studied 6,767 abdominal DECT images. The VMCT images reconstructed using both DL-derived DECT (DL-DECT) images and the images from DECT scanner were compared quantitatively. Paired-sample t-tests were used for statistical analysis to show the consistency and precision of calculated HU values. Results: Excellent agreement was found between the DL-DECT and the ground truth DECT images (p values ranged from 0.50 to 0.95). Noise reduction up to 68% (from 163 HU to 51 HU) was achieved for DL-based VMCT imaging as compared to that obtained by using the standard DECT. For the DL-based VMCT, the maximum iodine contrast-to-noise ratio (CNR) for each patient (ranging from 15.1 to 16.6) was achieved at 40 keV. In addition to the enormous benefit of VMCT acquisition with merely a SECT image, an improvement of CNR as high as 55% (from 10.7 to 16.6) was attained with the proposed approach. Conclusions: This study demonstrates that high-quality VMCT images can be obtained with only a SECT scan.
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