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Deep learning-accelerated image reconstruction in back pain-MRI imaging: reduction of acquisition time and improvement of image quality

Low back pain is a global health issue causing disability and missed work days. Commonly used MRI scans including T1-weighted and T2-weighted images provide detailed information of the spine and surrounding tissues. Artificial intelligence showed promise in improving image quality and simultaneously...

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Hauptverfasser: Estler, Arne (VerfasserIn) , Hauser, Till-Karsten (VerfasserIn) , Brunnée, Merle (VerfasserIn) , Zerweck, Leonie (VerfasserIn) , Richter, Vivien (VerfasserIn) , Knoppik, Jessica (VerfasserIn) , Örgel, Anja (VerfasserIn) , Bürkle, Eva (VerfasserIn) , Adib, Sasan Darius (VerfasserIn) , Hengel, Holger (VerfasserIn) , Nikolaou, Konstantin (VerfasserIn) , Ernemann, Ulrike (VerfasserIn) , Gohla, Georg (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 13 February 2024
In: La Radiologia medica
Year: 2024, Jahrgang: 129, Heft: 3, Pages: 478-487
ISSN:1826-6983
DOI:10.1007/s11547-024-01787-x
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s11547-024-01787-x
Volltext
Verfasserangaben:Arne Estler, Till-Karsten Hauser, Merle Brunnée, Leonie Zerweck, Vivien Richter, Jessica Knoppik, Anja Örgel, Eva Bürkle, Sasan Darius Adib, Holger Hengel, Konstantin Nikolaou, Ulrike Ernemann, Georg Gohla
Beschreibung
Zusammenfassung:Low back pain is a global health issue causing disability and missed work days. Commonly used MRI scans including T1-weighted and T2-weighted images provide detailed information of the spine and surrounding tissues. Artificial intelligence showed promise in improving image quality and simultaneously reducing scan time. This study evaluates the performance of deep learning (DL)-based T2 turbo spin-echo (TSE, T2DLR) and T1 TSE (T1DLR) in lumbar spine imaging regarding acquisition time, image quality, artifact resistance, and diagnostic confidence.
Beschreibung:Online veröffentlicht: 13. Februar 2024
Gesehen am 06.06.2024
Beschreibung:Online Resource
ISSN:1826-6983
DOI:10.1007/s11547-024-01787-x

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