ArticleNeuroradiology2026
Super-resolution deep learning reconstruction for brain fluid-attenuated inversion recovery: image quality and white matter hyperintensity volume analysis.
Article in Neuroradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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8 authors.
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Abstract
purposeTo evaluate whether super-resolution deep learning reconstruction (SR-DLR) improves two-dimensional (2D) brain fluid-attenuated inversion recovery (FLAIR) image quality while preserving automated white matter hyperintensity (WMH) volumetry, compared with Gaussian-filtered reconstruction (GA) and denoising DLR (dDLR).
methodsThirty-six healthy volunteers underwent 3T axial 2D FLAIR. Images were reconstructed using GA, dDLR, and SR-DLR (twofold in-plane upscaling). Quantitative metrics (noise, SNR, CNR, sharpness) were measured in standardized regions of interest. Two radiologists independently scored qualitative image quality. In a WMH-positive subgroup, WMH volumes were obtained using a transformer-based U-Net segmentation model on GA, dDLR, SR-DLR, and a high-resolution acquisition (HR-dDLR). Quantitative image-quality metrics were compared using one-way ANOVA, WMH volumes using paired t-tests, and interobserver agreement using weighted kappa.
resultsSR-DLR demonstrated the lowest image noise and the highest SNR, CNR, and sharpness compared with GA and dDLR (all p < 0.001). Qualitative scores for noise, sharpness, and overall image quality were significantly higher for SR-DLR (p < 0.001), with perfect inter-observer agreement for sharpness and overall quality. Mean WMH volumes did not differ significantly across reconstructions, and SR-DLR volumes closely matched those of HR-dDLR.
conclusionSR-DLR substantially improves 2D brain FLAIR image quality at 3T by reducing noise and increasing SNR, CNR, and sharpness while preserving WMH volumetry consistent with high-resolution reference standards.
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