Otgonbaatar Chuluunbaatar, Kim Hyunjung, Jeon Pil-Hyun, Jeon Sang-Hyun, Cha Sung-Jin, Ryu Jae-Kyun, Jung Won Beom, Shim Hackjoon, Ko Sung Min
Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Republic of Korea.
J Cardiovasc Imaging. 2024 Sep 20;32(1):30. doi: 10.1186/s44348-024-00031-4.
The recently introduced super-resolution (SR) deep learning image reconstruction (DLR) is potentially effective in reducing noise level and enhancing the spatial resolution. We aimed to investigate whether SR-DLR has advantages in the overall image quality and intensity homogeneity on coronary computed tomography (CT) angiography with four different approaches: filtered-back projection (FBP), hybrid iterative reconstruction (IR), DLR, and SR-DLR.
Sixty-three patients (mean age, 61 ± 11 years; range, 18-81 years; 40 men) who had undergone coronary CT angiography between June and October 2022 were retrospectively included. Image noise, signal to noise ratio, and contrast to noise ratio were quantified in both proximal and distal segments of the major coronary arteries. The left ventricle myocardium contrast homogeneity was analyzed. Two independent reviewers scored overall image quality, image noise, image sharpness, and myocardial homogeneity.
Image noise in Hounsfield units (HU) was significantly lower (P < 0.001) for the SR-DLR (11.2 ± 2.0 HU) compared to those associated with other image reconstruction methods including FBP (30.5 ± 10.5 HU), hybrid IR (20.0 ± 5.4 HU), and DLR (14.2 ± 2.5 HU) in both proximal and distal segments. SR-DLR significantly improved signal to noise ratio and contrast to noise ratio in both the proximal and distal segments of the major coronary arteries. No significant difference was observed in the myocardial CT attenuation with SR-DLR among different segments of the left ventricle myocardium (P = 0.345). Conversely, FBP and hybrid IR resulted in inhomogeneous myocardial CT attenuation (P < 0.001). Two reviewers graded subjective image quality with SR-DLR higher than other image reconstruction techniques (P < 0.001).
SR-DLR improved image quality, demonstrated clearer delineation of distal segments of coronary arteries, and was seemingly accurate for quantifying CT attenuation in the myocardium.
最近引入的超分辨率（SR）深度学习图像重建（DLR）在降低噪声水平和提高空间分辨率方面可能有效。我们旨在研究SR-DLR在冠状动脉计算机断层扫描（CT）血管造影中，与四种不同方法（滤波反投影（FBP）、混合迭代重建（IR）、DLR和SR-DLR）相比，在整体图像质量和强度均匀性方面是否具有优势。
回顾性纳入2022年6月至10月期间接受冠状动脉CT血管造影的63例患者（平均年龄61±11岁；范围18-81岁；40例男性）。对主要冠状动脉的近端和远端节段的图像噪声、信噪比和对比噪声比进行量化。分析左心室心肌对比均匀性。两名独立的审阅者对整体图像质量、图像噪声、图像清晰度和心肌均匀性进行评分。
与其他图像重建方法（包括FBP（30.5±10.5 HU）、混合IR（20.0±5.4 HU）和DLR（14.2±2.5 HU））相比，SR-DLR在近端和远端节段的Hounsfield单位（HU）图像噪声显著更低（P<0.001）。SR-DLR显著提高了主要冠状动脉近端和远端节段的信噪比和对比噪声比。在左心室心肌的不同节段中，SR-DLR在心肌CT衰减方面未观察到显著差异（P = 0.345）。相反，FBP和混合IR导致心肌CT衰减不均匀（P<0.001）。两名审阅者对主观图像质量的评分显示，SR-DLR高于其他图像重建技术（P<0.001）。
SR-DLR改善了图像质量，能更清晰地勾勒出冠状动脉的远端节段，并且在量化心肌CT衰减方面似乎是准确的。
