Deep learning-based image reconstruction significantly improves image quality of MRI examinations of the orbit at 3 Tesla - 23/11/25
, Augustin Lecler a, b, 1, Émilie Poirion a, Caroline Papeix c, Romain Deschamps c, Dan Milea d, Julien Savatovsky a, Loïc Duron a, Emma O’Shaughnessy aHighlights |
• | Deep learning-based image reconstruction significantly increases signal-to-noise ratio and contrast-to-noise ratio of coronal T2-weighted MR images of the orbit obtained at 3 Tesla. |
• | Deep learning-based image reconstruction improves optic nerve sharpness, brain sharpness and overall image quality of coronal T2-weighted and post-contrast fat-saturated T1-weighted MR images obtained at 3 Tesla. |
• | Diagnostic findings, including optic nerve hypersignal, enhancement, atrophy and orbital meningiomas, are preserved on both T2-weighted and post-contrast T1-weighted MR images obtained with deep learning-based reconstruction. |
Abstract |
Purpose |
The purpose of this study was to assess the benefit of a deep learning-based image reconstruction (DLBIR) for improving image quality in orbital magnetic resonance imaging (MRI) at 3 Tesla (T).
Materials and methods |
Seventy-one patients (48 women and 23 men) with a mean age of 52 ± 19.5 (standard deviation [SD]) years (age range: 7–90 years) who underwent MRI examination of the orbit at 3 T between January and June of 2024, were included in the study. Coronal T2-weighted MR images obtained in 70 patients and post-contrast fat-saturated (FS) coronal T1-weighted MR images obtained in 25 patients, were reconstructed with and without DLBIR, resulting in four imaging sets. Two radiologists independently and blindly measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the optic nerves on the four imaging sets. Image quality and orbital abnormalities were assessed using a standardized 5-point Likert scale. Comparisons between MR images obtained with and without DLBIR were performed using Wilcoxon test for ordinal and quantitative variables and McNemar test for paired binary data.
Results |
SNR and CNR of coronal T2-weighted MR images were significantly greater using DLBIR (26.67 ± 9.03 [SD], and 14.87 ± 10.31 [SD], respectively) than without DLBIR (18.91 ± 7.28 [SD], and 9.78 ± 8.47 [SD], respectively) (P < 0.001). There were no differences in SNR and CNR between post-contrast FS T1-weighted images obtained with DLBIR (85.56 ± 63.13 [SD], and 64 ± 41.38 [SD], respectively) and those obtained without DLBIR (91.36 ± 48.49 [SD], and 43.25 ± 20.4 [SD], respectively) (P = 0.35, and P = 0.14, respectively). Qualitatively, good-to-excellent image quality was obtained more frequently with DLBIR than without DLBIR for T2-weighted and post-contrast FS T1-weighted images with respect to optic nerve sharpness (67 % vs. 16 %, and 8 % vs. 0 %, respectively), brain sharpness (90 % vs. 6 %, and 68 % vs. 4 %, respectively), and overall image quality (73 % vs. 1 % and 36 % vs. 0 %, respectively) (all P ≤ 0.001). No significant differences in the detection rates of orbital abnormalities were found between MR images obtained with and without DLBIR, including optic nerve hyperintensity (34 % vs. 31 %, respectively; P = 0.16) and optic nerve atrophy (33 % for both) on T2-weighted images, and optic nerve enhancement on post-contrast FS T1-weighted images (16 % for both).
Conclusion |
DLBIR significantly improves image quality of MRI examinations of the orbit at 3 T, without losing clinically relevant information.
Il testo completo di questo articolo è disponibile in PDF.Keywords : Deep learning, Deep learning-based image reconstruction, Image processing, Magnetic resonance imaging, Optic nerve
Abbreviations : CNR, DLBIR, FS, ICC, MRI, ROI, SD, SNR
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