Asan Medical Center, Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea.
Pediatr Radiol. 2020 Oct;50(11):1570-1578. doi: 10.1007/s00247-020-04741-x. Epub 2020 Jun 26.
Image sharpness is commonly degraded on cardiac CT images reconstructed using iterative reconstruction algorithms.
To compare the image quality of cardiac CT between raw-data-based and model-based iterative reconstruction algorithms developed by the same CT vendor in children and young adults with congenital heart disease.
In 29 patients with congenital heart disease, we reconstructed 39 cardiac CT datasets using raw-data-based and model-based iterative reconstruction algorithms. We performed quantitative analysis of image sharpness using distance and angle on a line density profile across an edge of the descending thoracic aorta in addition to CT attenuation, image noise, signal-to-noise ratio and contrast-to-noise ratio. We compared these quantitative image-quality measures between the two algorithms.
CT attenuation did not show significant differences between the algorithms (P>0.05) except in the aorta. Image noise was small but significantly higher in the model-based algorithm than in the raw-data-based algorithm (4.8±2.3 Hounsfield units [HU] vs. 4.7±2.1 HU, P<0.014). Signal-to-noise ratio (110.2±50.9 vs. 108.4±50.4, P=0.050) and contrast-to-noise ratio (91.0±45.7 vs. 89.6±45.1, P=0.063) showed marginal significance between the two algorithms. The model-based algorithm showed a significantly smaller distance (1.4±0.4 mm vs. 1.6±0.3 mm, P<0.001) and a significantly higher angle (77.0±4.3° vs. 74.1±5.7°, P<0.001) than the raw-data-based algorithm.
Compared with the raw-data-based algorithm, the model-based iterative reconstruction algorithm demonstrated better image sharpness and higher image noise on cardiac CT in patients with congenital heart disease.
在使用迭代重建算法重建的心脏 CT 图像中，图像锐度通常会降低。
比较同一家 CT 供应商开发的基于原始数据和基于模型的迭代重建算法在患有先天性心脏病的儿童和年轻成人心脏 CT 中的图像质量。
在 29 例先天性心脏病患者中，我们使用基于原始数据和基于模型的迭代重建算法重建了 39 组心脏 CT 数据集。我们使用穿过降主动脉边缘的线密度轮廓对图像锐度进行了定量分析，除了 CT 衰减外，还包括图像噪声、信噪比和对比噪声比。我们比较了这两种算法之间的这些定量图像质量指标。
除了主动脉外，两种算法的 CT 衰减没有显著差异（P>0.05）。图像噪声较小，但基于模型的算法明显高于基于原始数据的算法（4.8±2.3 亨氏单位[HU]与 4.7±2.1 HU，P<0.014）。信噪比（110.2±50.9 与 108.4±50.4，P=0.050）和对比噪声比（91.0±45.7 与 89.6±45.1，P=0.063）在两种算法之间显示出边缘显著性。基于模型的算法的距离（1.4±0.4mm 与 1.6±0.3mm，P<0.001）和角度（77.0±4.3°与 74.1±5.7°，P<0.001）明显小于基于原始数据的算法。
与基于原始数据的算法相比，基于模型的迭代重建算法在患有先天性心脏病的患者的心脏 CT 中显示出更好的图像锐度和更高的图像噪声。
