Abstract：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.
王怡然，詹鹤凤，吴文杰，侯佳蒙，马雪妍，张永高. 基于深度学习的重建算法在CCTA高分辨率成像中的应用[J]. 中国医疗设备, 2021, 36(10): 24-27.
WANG Yiran, ZHAN Hefeng, WU Wenjie, HOU Jiameng, MA Xueyan, ZHANG Yonggao. Application of Deep Learning-Based Reconstruction Algorithms for CCTA High-Resolution Imaging. China Medical Devices, 2021, 36(10): 24-27.
