Sun Zhonghua, Ng Curtise K C
Discipline of Medical Radiation Science, Curtin Medical School, Curtin University, P.O. Box U1987, Perth, WA 6845, Australia.
Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University, P.O. Box U1987, Perth, WA 6845, Australia.
Diagnostics (Basel). 2022 Apr 14;12(4):991. doi: 10.3390/diagnostics12040991.
The presence of heavy calcification in the coronary artery always presents a challenge for coronary computed tomography angiography (CCTA) in assessing the degree of coronary stenosis due to blooming artifacts associated with calcified plaques. Our study purpose was to use an advanced artificial intelligence (enhanced super-resolution generative adversarial network [ESRGAN]) model to suppress the blooming artifact in CCTA and determine its effect on improving the diagnostic performance of CCTA in calcified plaques.
A total of 184 calcified plaques from 50 patients who underwent both CCTA and invasive coronary angiography (ICA) were analysed with measurements of coronary lumen on the original CCTA, and three sets of ESRGAN-processed images including ESRGAN-high-resolution (ESRGAN-HR), ESRGAN-average and ESRGAN-median with ICA as the reference method for determining sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
ESRGAN-processed images improved the specificity and PPV at all three coronary arteries (LAD-left anterior descending, LCx-left circumflex and RCA-right coronary artery) compared to original CCTA with ESRGAN-median resulting in the highest values being 41.0% (95% confidence interval [CI]: 30%, 52.7%) and 26.9% (95% CI: 22.9%, 31.4%) at LAD; 41.7% (95% CI: 22.1%, 63.4%) and 36.4% (95% CI: 28.9%, 44.5%) at LCx; 55% (95% CI: 38.5%, 70.7%) and 47.1% (95% CI: 38.7%, 55.6%) at RCA; while corresponding values for original CCTA were 21.8% (95% CI: 13.2%, 32.6%) and 22.8% (95% CI: 20.8%, 24.9%); 12.5% (95% CI: 2.6%, 32.4%) and 27.6% (95% CI: 24.7%, 30.7%); 17.5% (95% CI: 7.3%, 32.8%) and 32.7% (95% CI: 29.6%, 35.9%) at LAD, LCx and RCA, respectively. There was no significant effect on sensitivity and NPV between the original CCTA and ESRGAN-processed images at all three coronary arteries. The area under the receiver operating characteristic curve was the highest with ESRGAN-median images at the RCA level with values being 0.76 (95% CI: 0.64, 0.89), 0.81 (95% CI: 0.69, 0.93), 0.82 (95% CI: 0.71, 0.94) and 0.86 (95% CI: 0.76, 0.96) corresponding to original CCTA and ESRGAN-HR, average and median images, respectively.
This feasibility study shows the potential value of ESRGAN-processed images in improving the diagnostic value of CCTA for patients with calcified plaques.
冠状动脉中重度钙化的存在，在冠状动脉计算机断层扫描血管造影（CCTA）评估冠状动脉狭窄程度时，总是会带来挑战，因为钙化斑块会产生 blooming 伪影。我们的研究目的是使用一种先进的人工智能（增强超分辨率生成对抗网络[ESRGAN]）模型来抑制 CCTA 中的 blooming 伪影，并确定其对提高 CCTA 在钙化斑块诊断性能方面的效果。
对 50 例同时接受了 CCTA 和有创冠状动脉造影（ICA）的患者的 184 个钙化斑块进行分析，在原始 CCTA 上测量冠状动脉管腔，并以 ICA 作为确定敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）的参考方法，对包括 ESRGAN 高分辨率（ESRGAN-HR）、ESRGAN 平均值和 ESRGAN 中值的三组 ESRGAN 处理后的图像进行分析。
与原始 CCTA 相比，ESRGAN 处理后的图像在所有三支冠状动脉（左前降支[LAD]、左旋支[LCx]和右冠状动脉[RCA]）均提高了特异性和 PPV，ESRGAN 中值处理后的图像在 LAD 处的值最高，分别为 41.0%（95%置信区间[CI]：30%，52.7%）和 26.9%（95%CI：22.9%，31.4%）；在 LCx 处为 41.7%（95%CI：22.1%，63.4%）和 36.4%（95%CI：28.9%，44.5%）；在 RCA 处为 55%（95%CI：38.5%，70.7%）和 47.1%（95%CI：38.7%，55.6%）；而原始 CCTA 在 LAD、LCx 和 RCA 处的相应值分别为 21.8%（95%CI：13.2%，32.6%）和 22.8%（95%CI：20.8%，24.9%）；12.5%（95%CI：2.6%，32.4%）和 27.6%（95%CI：24.7%，30.7%）；17.5%（95%CI：7.3%，32.8%）和 32.7%（95%CI：29.6%，35.9%）。原始 CCTA 和 ESRGAN 处理后的图像在所有三支冠状动脉的敏感性和 NPV 方面均无显著影响。在 RCA 水平，ESRGAN 中值图像的受试者操作特征曲线下面积最高，对应原始 CCTA 和 ESRGAN-HR、平均值和中值图像的值分别为 0.76（95%CI：0.64，0.89）、0.81（95%CI：0.69，0.93）、0.82（95%CI：0.71，0.94）和 0.86（95%CI：0.76，0.96）。
这项可行性研究表明，ESRGAN 处理后的图像在提高 CCTA 对钙化斑块患者的诊断价值方面具有潜在价值。
