ArticleSkeletal radiology2026
Deep learning-based chemical shift-artifact correction of ZTE MRI for enhanced bone depiction of the lumbar spine.
Article in Skeletal 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
RATIONALE AND
objectivesZero echo time (ZTE) is an advanced MRI technique providing CT-like images of mineralized tissues. This study evaluates the impact of deep learning (DL)-based reconstruction with chemical shift correction (DLCSC) on osseous depiction in ZTE MRI of the lumbar spine, compared to standard DL and non-DL reconstruction.
methodsThis retrospective, single-center study included 38 patients undergoing 3 T ZTE MRI of the lumbar spine. K-space data were reconstructed using three methods: non-DL, standard DL, and a prototype DLCSC algorithm. Quantitative image sharpness, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were analyzed using repeated-measures ANOVA. Two radiologists independently rated pathology-related criteria (n = 22) and bone depiction quality (n = 38) on a 4-point scale. Ordinal data were analyzed using the Friedman test, and inter-reader agreement was assessed with weighted Cohen's kappa.
resultsDLCSC images yielded quantitatively sharper images compared to non-DL (p = 0.010) and standard DL (p < 0.001). SNR and CNR were significantly higher in DLCSC and DL than in non-DL (p < 0.001). In the qualitative assessment, mean scores for all criteria of pathologies and bone depiction quality improved significantly from non-DL and DL to DLCSC (p < 0.001). There was no evidence of differences in classification of pathologies (p > 0.05). Inter-reader agreement ranged from substantial to almost perfect (κ = 0.867-0.901).
conclusionDLCSC image reconstruction of ZTE MRI can improve bone depiction of the lumbar spine compared to standard DL and non-DL reconstruction.
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