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| Verfasst von: | Estler, Arne [VerfasserIn]  |
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| | Zerweck, Leonie [VerfasserIn] |
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| | Brunnée, Merle [VerfasserIn] |
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| | Estler, Bent [VerfasserIn] |
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| | Richter, Vivien [VerfasserIn] |
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| | Örgel, Anja [VerfasserIn] |
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| | Bürkle, Eva [VerfasserIn] |
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| | Becker, Hannes [VerfasserIn] |
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| | Hurth, Helene [VerfasserIn] |
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| | Stahl, Stéphane [VerfasserIn] |
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| | Konrad, Eva-Maria [VerfasserIn] |
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| | Kelbsch, Carina [VerfasserIn] |
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| | Ernemann, Ulrike [VerfasserIn] |
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| | Hauser, Till-Karsten [VerfasserIn] |
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| | Gohla, Georg [VerfasserIn] |
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| Titel: | Deep learning-accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality |
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| Verf.angabe: | Arne Estler, Leonie Zerweck, Merle Brunnée, Bent Estler, Vivien Richter, Anja Örgel, Eva Bürkle, Hannes Becker, Helene Hurth, Stéphane Stahl, Eva-Maria Konrad, Carina Kelbsch, Ulrike Ernemann, Till-Karsten Hauser, Georg Gohla |
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| E-Jahr: | 2024 |
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| Jahr: | Mar 2024 |
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| Umfang: | 9 S. |
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| Illustrationen: | Illustrationen, Diagramme |
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| Fussnoten: | Gesehen am 10.06.2024 |
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| Titel Quelle: | Enthalten in: Journal of neuroimaging |
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| Ort Quelle: | Berlin [u.a.] : Wiley-Blackwell, 1991 |
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| Jahr Quelle: | 2024 |
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| Band/Heft Quelle: | 34(2024), 2, Seite 232-240 |
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| ISSN Quelle: | 1552-6569 |
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| Abstract: | Background and Purpose This study explores the use of deep learning (DL) techniques in MRI of the orbit to enhance imaging. Standard protocols, although detailed, have lengthy acquisition times. We investigate DL-based methods for T2-weighted and T1-weighted, fat-saturated, contrast-enhanced turbo spin echo (TSE) sequences, aiming to improve image quality, reduce acquisition time, minimize artifacts, and enhance diagnostic confidence in orbital imaging. Methods In a 3-Tesla MRI study of 50 patients evaluating orbital diseases from March to July 2023, conventional (TSES) and DL TSE sequences (TSEDL) were used. Two neuroradiologists independently assessed the image datasets for image quality, diagnostic confidence, noise levels, artifacts, and image sharpness using a randomized and blinded 4-point Likert scale. Results TSEDL significantly reduced image noise and artifacts, enhanced image sharpness, and decreased scan time, outperforming TSES (p < .05). TSEDL showed superior overall image quality and diagnostic confidence, with relevant findings effectively detected in both DL-based and conventional images. In 94% of cases, readers preferred accelerated imaging. Conclusion The study proved that using DL for MRI image reconstruction in orbital scans significantly cut acquisition time by 69%. This approach also enhanced image quality, reduced image noise, sharpened images, and boosted diagnostic confidence. |
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| DOI: | doi:10.1111/jon.13187 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1111/jon.13187 |
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| | Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/jon.13187 |
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| | DOI: https://doi.org/10.1111/jon.13187 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | acquisition time |
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| | deep learning reconstruction |
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| | deep resolve boost |
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| | image processing |
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| | image quality |
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| | magnetic resonance imaging |
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| | orbital imaging |
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| K10plus-PPN: | 1891018345 |
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| Verknüpfungen: | → Zeitschrift |
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Deep learning-accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality / Estler, Arne [VerfasserIn]; Mar 2024 (Online-Ressource)
