ArticleClinical neuroradiology2026
Deep Learning Reconstruction of Diffusion-weighted MRI Enables Shorter Examination Times While Maintaining Image Quality in Head and Neck Imaging.
Article in Clinical neuroradiology, 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
objectivesDiffusion-weighted imaging (DWI) of the head and neck is essential for various clinical applications but is often hampered by artifacts and reduced image quality. Deep learning (DL) reconstruction has the potential to enhance the quality of head and neck DWI. This study aims to evaluate the performance of an accelerated, DL-reconstructed DWI (DWI MATERIALS AND
methodsThis retrospective study included patients who underwent clinically indicated head and neck DWI at 1.5 T and 3 T between August 2023 and January 2024 at a tertiary care center. Imaging was performed at low b‑values (0 or 50 sec/mm
resultsThe study included 30 patients (mean age, 55 ± 19 years; range, 24-84; 18 men) with various pathologies. Scan times were reduced by 67% at 1.5 T and up to 55% at 3 T. The quantitative analysis revealed a minimal but statistically significant decrease in SNR and CNR in the deep learning-reconstructed images (p = 0.002 and p < 0.001, respectively). However, readers reported no significant differences between DWI and DWI
conclusionDL reconstruction of head and neck DWI is feasible, significantly reducing examination time without compromising image quality or diagnostic confidence. This technique enables accelerated and effective diagnostic DWI of the head and neck.
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