Department of Radiology and Medical Research Institute, College of Medicine, Ewha Womans University Seoul Hospital, Seoul, Republic of Korea.
Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, South Korea.
PLoS One. 2024 Aug 26;19(8):e0300090. doi: 10.1371/journal.pone.0300090. eCollection 2024.
To evaluate the quantitative and qualitative image quality using deep learning image reconstruction (DLIR) of pediatric cardiac computed tomography (CT) compared with conventional image reconstruction methods.
Between January 2020 and December 2022, 109 pediatric cardiac CT scans were included in this study. The CT scans were reconstructed using an adaptive statistical iterative reconstruction-V (ASiR-V) with a blending factor of 80% and three levels of DLIR with TrueFidelity (low-, medium-, and high-strength settings). Quantitative image quality was measured using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The edge rise distance (ERD) and angle between 25% and 75% of the line density profile were drawn to evaluate sharpness. Qualitative image quality was assessed using visual grading analysis scores.
A gradual improvement in the SNR and CNR was noted among the strength levels of the DLIR in sequence from low to high. Compared to ASiR-V, high-level DLIR showed significantly improved SNR and CNR (P<0.05). ERD decreased with increasing angle as the level of DLIR increased.
High-level DLIR showed improved SNR and CNR compared to ASiR-V, with better sharpness on pediatric cardiac CT scans.
本研究旨在评估基于深度学习的图像重建（DLIR）与传统图像重建方法在小儿心脏 CT 中的定量和定性图像质量。
本研究纳入了 2020 年 1 月至 2022 年 12 月期间的 109 例小儿心脏 CT 扫描。采用自适应统计迭代重建-V（ASiR-V）以 80%的混合因子和 3 个级别的 DLIR（低、中、高强度设置）进行 CT 重建。使用信噪比（SNR）和对比噪声比（CNR）来测量定量图像质量。通过绘制边缘上升距离（ERD）和线密度曲线 25%至 75%之间的角度来评估锐利度。使用视觉分级分析评分评估定性图像质量。
在从低到高的 DLIR 强度级别中，SNR 和 CNR 逐渐提高。与 ASiR-V 相比，高强度的 DLIR 显著提高了 SNR 和 CNR（P<0.05）。随着 DLIR 水平的增加，ERD 随角度的增加而减小。
与 ASiR-V 相比，高强度的 DLIR 显示出更好的 SNR 和 CNR，在小儿心脏 CT 扫描中具有更好的锐利度。
