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Objective <p>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).</p> Materials and methods <p>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.</p> Results <p>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; <i>p</i> &lt; 0.001) and HIR (8.98 ± 6.37 mm; <i>p</i> &lt; 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 (<i>p</i> &lt; 0.05). Subjective evaluations favored SR-DLR over C-DLR and HIR (<i>p</i> &lt; 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 (<i>p</i> &lt; 0.05 and <i>p</i> &lt; 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.</p> Conclusion <p>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.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Pediatric cardiac computed tomography angiography (CCTA) often fails to adequately visualize intracardiac structures, creating diagnostic challenges for CHD, particularly complex multi-perforated atrioventricular defects</i>.</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>SR-DLR markedly improves image quality and diagnostic accuracy, enabling detailed visualization and precise detection of small congenital lesions</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>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</i>.</p> Graphical Abstract <p></p>

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Super-resolution deep learning in pediatric CTA for congenital heart disease: enhancing intracardiac visualization under free-breathing conditions

  • 期刊论文

  • 发表日期：

    2025年7月16日

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  • Xinyan Zhou,
  • Daoqiang Xiong,
  • Fei Liu,
  • Junyi Li,
  • Na Tan,
  • Xirui Duan,
  • Xiaolan Du,
  • Zhiqiang Ouyang,
  • Shasha Bao,
  • Tengfei Ke,
  • Yihan Zhao,
  • Jianxiang Tao,
  • Xuemin Dong,
  • Yining Wang,
  • Chengde Liao

摘要

Objective

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).

Materials and methods

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.

Results

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.

Conclusion

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.

Key Points

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.

Graphical Abstract