Zhou Xinyan, Xiong Daoqiang, Liu Fei, Li Junyi, Tan Na, Duan Xirui, Du Xiaolan, Ouyang Zhiqiang, Bao Shasha, Ke Tengfei, Zhao Yihan, Tao Jianxiang, Dong Xuemin, Wang Yining, Liao Chengde
Department of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
The First Affiliated Hospital of Kunming Medical University, Kunming, China.
Eur Radiol. 2025 Jul 16. doi: 10.1007/s00330-025-11800-0.
This study assesses the effectiveness of super-resolution deep learning reconstruction (SR-DLR), conventional deep learning reconstruction (C-DLR), and hybrid iterative reconstruction (HIR) in enhancing image quality and diagnostic performance for pediatric congenital heart disease (CHD) in CT angiography (CCTA).
A total of 91 pediatric patients aged 1-10 years, suspected of having CHD, were consecutively enrolled for CCTA under free-breathing conditions. Reconstructions were performed using SR-DLR, C-DLR, and HIR algorithms. Objective metrics-standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)-were quantified. Two radiologists provided blinded subjective image quality evaluations.
The full width at half maximum of lesions was significantly larger on SR-DLR (9.50 ± 6.44 mm) than on C-DLR (9.08 ± 6.23 mm; p < 0.001) and HIR (8.98 ± 6.37 mm; p < 0.001). SR-DLR exhibited superior performance with significantly reduced SD and increased SNR and CNR, particularly in the left ventricle, left atrium, and right ventricle regions (p < 0.05). Subjective evaluations favored SR-DLR over C-DLR and HIR (p < 0.05). The accuracy (99.12%), sensitivity (99.07%), and negative predictive value (85.71%) of SR-DLR were the highest, significantly exceeding those of C-DLR (+7.01%, +7.40%, and +45.71%) and HIR (+20.17%, +21.29%, and +65.71%), with statistically significant differences (p < 0.05 and p < 0.001). In the detection of atrial septal defects (ASDs) and ventricular septal defects (VSDs), SR-DLR demonstrated significantly higher sensitivity compared to C-DLR (+8.96% and +9.09%) and HIR (+20.90% and +36.36%). For multi-perforated ASDs and VSDs, SR-DLR's sensitivity reached 85.71% and 100%, far surpassing C-DLR and HIR.
SR-DLR significantly reduces image noise and enhances resolution, improving the diagnostic visualization of CHD structures in pediatric patients. It outperforms existing algorithms in detecting small lesions, achieving diagnostic accuracy close to that of ultrasound.
Question Pediatric cardiac computed tomography angiography (CCTA) often fails to adequately visualize intracardiac structures, creating diagnostic challenges for CHD, particularly complex multi-perforated atrioventricular defects. Findings SR-DLR markedly improves image quality and diagnostic accuracy, enabling detailed visualization and precise detection of small congenital lesions. Clinical relevance SR-DLR enhances the diagnostic confidence and accuracy of CCTA in pediatric CHD, reducing missed diagnoses and improving the characterization of complex intracardiac anomalies, thus supporting better clinical decision-making.
本研究评估超分辨率深度学习重建（SR-DLR）、传统深度学习重建（C-DLR）和混合迭代重建（HIR）在提高小儿先天性心脏病（CHD）CT血管造影（CCTA）图像质量和诊断性能方面的有效性。
连续纳入91例年龄在1至10岁、疑似患有CHD的小儿患者，在自由呼吸条件下进行CCTA检查。使用SR-DLR、C-DLR和HIR算法进行重建。对客观指标——标准差（SD）、信噪比（SNR）和对比噪声比（CNR）进行量化。两名放射科医生进行盲法主观图像质量评估。
SR-DLR上病变的半高全宽（9.50±6.44mm）显著大于C-DLR（9.08±6.23mm；p<0.001）和HIR（8.98±6.37mm；p<0.001）。SR-DLR表现出卓越性能，SD显著降低，SNR和CNR增加，尤其是在左心室、左心房和右心室区域（p<0.05）。主观评估显示，SR-DLR优于C-DLR和HIR（p<0.05）。SR-DLR的准确率（99.12%）、灵敏度（99.07%）和阴性预测值（85.71%）最高，显著超过C-DLR（分别高出7.01%、7.40%和45.71%）和HIR（分别高出20.17%、21.29%和65.71%），差异具有统计学意义（p<0.05和p<0.001）。在房间隔缺损（ASD）和室间隔缺损（VSD）的检测中，SR-DLR的灵敏度显著高于C-DLR（分别高出8.96%和9.09%）和HIR（分别高出20.90%和36.36%）。对于多发孔型ASD和VSD，SR-DLR的灵敏度分别达到85.71%和100%，远远超过C-DLR和HIR。
SR-DLR显著降低图像噪声并提高分辨率，改善小儿患者CHD结构的诊断可视化。在检测小病变方面，它优于现有算法，诊断准确率接近超声。
问题小儿心脏CT血管造影（CCTA）常常无法充分显示心内结构，给CHD带来诊断挑战，尤其是复杂的多发孔型房室缺损。发现SR-DLR显著提高图像质量和诊断准确性，能够详细显示并精确检测小的先天性病变。临床意义SR-DLR增强了CCTA在小儿CHD中的诊断信心和准确性，减少漏诊并改善复杂心内异常的特征描述，从而支持更好的临床决策。
