ArticleJournal of imaging informatics in medicine2026
Image Quality Advancements in Low-Dose Pediatric CT Using Super-Resolution Deep-Learning Reconstruction.
Article in Journal of imaging informatics in medicine, 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
The objective of the study is to assess the impact of super-resolution deep-learning reconstruction (SR-DLR) on image quality in low-dose thin-slice pediatric abdominal CT. Thirty-eight children (< 10 years; median age, 2.0 [IQR: 0.0-3.8] years) who had available low-radiation abdominal CT data were retrospectively analyzed. The mean size-specific dose estimate was 1.71 ± 0.38 mGy. From the raw datasets, 0.5-mm images were generated using SR-DLR developed for helical body imaging as well as hybrid-iterative reconstruction (hybrid-IR) and conventional deep-learning reconstruction (C-DLR), with matrices of 1024, 512, and 512, respectively. Quantitative assessments included measurements of image noise and contrast-to-noise ratio (CNR). Image noise characteristics were evaluated using the noise power spectrum (NPS), while edge sharpness was evaluated based on the edge-rise slope (ERS). For qualitative evaluation, noise magnitude, texture, sharpness, and small structure delineation were ranked among three reconstructions (1 = worst, 3 = best). Diagnostic confidence was scored with a five-point scale (1 = undiagnostic, 5 = most confident). Quantitative and qualitative data were compared among hybrid-IR, C-DLR, and SR-DLR. SR-DLR showed the greatest noise reduction and the highest CNR, outperforming the other reconstructions (p < 0.001). SR-DLR also achieved the highest ERS and maintained the lowest noise throughout the full range of spatial frequencies without a shift toward lower frequency. Subjective scores aligned with these quantitative findings, with SR-DLR consistently achieving the highest scores across all qualitative metrics (all p < 0.001). In conclusion, SR-DLR with 1024-matrix for helical body imaging enhanced sharpness and reduced noise in low-dose thin-slice pediatric abdominal CT, outperforming hybrid-IR and C-DLR in small structure delineation and overall diagnostic quality.
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