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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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
13 February 2024
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| In: |
La Radiologia medica
Year: 2024, Volume: 129, Issue: 3, Pages: 478-487 |
| ISSN: | 1826-6983 |
| DOI: | 10.1007/s11547-024-01787-x |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s11547-024-01787-x
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| Author Notes: | 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 |
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| 520 | |a 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. | ||
| 650 | 4 | |a Acquisition time | |
| 650 | 4 | |a Back pain | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Deep resolve boost | |
| 650 | 4 | |a Image quality | |
| 650 | 4 | |a MRI | |
| 650 | 4 | |a Spine imaging | |
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| 700 | 1 | |a Ernemann, Ulrike |e VerfasserIn |4 aut | |
| 700 | 1 | |a Gohla, Georg |e VerfasserIn |4 aut | |
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