ArticlePediatric radiology2026
Super-resolution deep learning reconstruction achieves low-dose high-resolution imaging in pediatric cardiac computed tomography.
Article in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCardiac computed tomography (CT) is widely utilized in pediatric cardiology, but minimizing radiation exposure is essential. Recently, super-resolution deep learning reconstruction (SR-DLR) has emerged as a potential advancement over conventional deep learning reconstruction (C-DLR).
objectiveTo evaluate the potential of SR-DLR for further radiation dose reduction in pediatric cardiac CT through a comparison with C-DLR. MATERIALS AND
methodsThe phantom-based study assessed noise power spectrum and spatial resolution. For the clinical study, we compared nine C-DLR images (conventional radiation dose) and 11 SR-DLR images (lower radiation dose). Evaluation metrics included noise levels, contrast-to-noise ratio, and edge rise distance and edge rise slope.
resultsIn the phantom-based study, SR-DLR (250 mA) displayed equivalent noise characteristics as C-DLR (800 mA) while achieving superior spatial resolution. In clinical images, preliminary comparisons within the high-dose group revealed that SR-DLR significantly outperformed C-DLR in spatial resolution metrics, specifically edge rise distance and edge rise slope (P<0.001). Furthermore, despite the lower radiation dose used for SR-DLR than for C-DLR (volume CT dose index, 4.25±0.54 mGy vs. 13.3±6.43 mGy), there were no significant differences in noise, contrast resolution, and spatial resolution between the groups.
conclusionSR-DLR maintains non-inferior image quality compared with C-DLR even at reduced radiation doses. These findings substantiate the potential for further dose reduction in pediatric cardiac CT without compromising image quality.
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