基于深度学习的重建算法在CCTA高分辨率成像中的应用

Application of Deep Learning-Based Reconstruction Algorithms for CCTA High-Resolution Imaging

目的探讨深度学习图像重建(Deep Learning Image Reconstruction,DLIR)对高分辨率CT血管成像图像质量和诊断准确性的影响。方法前瞻性纳入56例患者在GE Revolution CT以高分辨率模式进行冠状动脉CT血管成像(Coronary CT Angiography,CCTA),分别使用50%权重的自适应迭代重建技术(Adaptive Statistical Iterative Reconstruction,ASIR)-V和中级别(DLIR-M)、高级别(DLIR-H)重建原始数据(应用高清卷积核)。记录主动脉根部和主要冠状动脉近段的图像噪声,并计算信噪比(Signal to Noise Ratio,SNR)和对比噪声比(Contrast to Noise Ratio,CNR)以客观评价图像质量。在32例患者亚组中,比较ASIR-V 50%、DLIR-M和DLIR-H对冠状动脉狭窄的诊断准确率,并与有创冠状动脉造影进行比较。主观图像质量由两名有5年以上经验的影像诊断医师按5分制进行评级。结果与ASIR-V 50%相比,DLIR-H和DLIR-M的噪声分别降低了54.8%和59.9%(P值均<0.05);SNR和CNR显著增加,主观图像质量较ASIR-V 50%有显著改善(P值均<0.05)。DLIR-H和DLIR-M对冠状动脉狭窄的诊断准确性无显著影响。结论与ASIR-V相比,DLIR能提升高分辨率模式下CCTA图像的整体质量,并不影响对冠状动脉阻塞性心脏病的诊断准确性。

Objective To explore the influence of deep learning image reconstruction(DLIR)on image quality and diagnostic accuracy of high-resolution CT angiography.Methods 56 patients underwent coronary computed tomography angiography(CCTA)on a high-definition CT(Revolution CT,GE Healthcare).Data sets were reconstructed with ASiR-V 50%(applying HD kernels)and with DLIR at medium and high settings(DLIR-M and DLIR-H),respectively.The image noise,signal to noise ratio(SNR),and contrast to noise ratio(CNR)on aorta root and main coronary proximal segments were calculated to evaluate image quality objectively.In a subgroup of 32 patients,diagnostic accuracy of ASiR-V 50%,DLIR-M and DLIR-H for diagnosis of coronary artery disease(CAD)were compared with invasive coronary angiography.Subjective image quality was blindly graded by two imaging diagnosticians with over 5 years of experience on a five-point scale.Results The noise of DLIR-M and DLIR-H were significantly decreased by 54.8%and 59.9%,while SNR and CNR were significantly increased compared to ASiR-V 50%(P<0.05).The subjective image quality improved significantly(P<0.05).DLIR-H and DLIR-M had no significant effect on the diagnostic accuracy of coronary artery stenosis.Conclusion Compared with ASIR-V,DLIR could effectively improve the overall image quality of CCTA images in high-resolution mode.and has no effect on diagnostic accuracy of CCTA for CAD detection.