论文标题
使用标准单能CT扫描仪的虚拟单色光谱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
论文作者
论文摘要
目的/目标：制定和评估使用深度学习（DL）从单能CT（SECT）扫描生成虚拟单色CT（VMCT）图像的策略。材料/方法：提议的数据驱动的VMCT成像包括两个步骤：（i）使用有大量100 kV和140 kV双能量CT（DECT）图像对训练的有监督的DL模型，以产生来自低功能图像的相应的高能量CT图像； （ii）重建功能范围从40到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个腹部DECT图像。使用DL衍生的DECT（DL-DECT）图像和DECT扫描仪的图像重建的VMCT图像被定量比较。配对样本t检验用于统计分析，以显示计算出的HU值的一致性和精度。结果：在DL-DECT和地面真实DECT图像之间发现了极好的一致性（P值范围为0.50至0.95）。与使用标准DECT获得的基于DL的VMCT成像相比，基于DL的VMCT成像可降低高达68％（从163 HU到51 HU）。对于基于DL的VMCT，在40 KEV时实现了每个患者的最大碘对比度比（CNR）（范围从15.1到16.6）。除了仅使用教派图像获得VMCT获取的巨大益处外，CNR的提高高达55％（从10.7到16.6），还通过了拟议的方法获得。结论：这项研究表明，仅通过扫描可以获得高质量的VMCT图像。
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.
