Deep learning accelerated image reconstruction of fluid-attenuated inversion recovery sequence in brain imaging: reduction of acquisition time and improvement of image quality
Rationale and Objectives - Fluid-attenuated inversion recovery (FLAIR) imaging is playing an increasingly significant role in the detection of brain metastases with a concomitant increase in the number of magnetic resonance imaging (MRI) examinations. Therefore, the purpose of this study was to inve...
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| Hauptverfasser: | , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
January 2024
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| In: |
Academic radiology
Year: 2024, Jahrgang: 31, Heft: 1, Pages: 180-186 |
| ISSN: | 1878-4046 |
| DOI: | 10.1016/j.acra.2023.05.010 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.acra.2023.05.010 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S1076633223002465 
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| Verfasserangaben: | Arne Estler, Till-Karsten Hauser, Annerose Mengel, Merle Brunnée, Leonie Zerweck, Vivien Richter, Mario Zuena, Martin Schuhholz, Ulrike Ernemann, Georg Gohla |
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| 245 | 1 | 0 | |a Deep learning accelerated image reconstruction of fluid-attenuated inversion recovery sequence in brain imaging |b reduction of acquisition time and improvement of image quality |c Arne Estler, Till-Karsten Hauser, Annerose Mengel, Merle Brunnée, Leonie Zerweck, Vivien Richter, Mario Zuena, Martin Schuhholz, Ulrike Ernemann, Georg Gohla |
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| 520 | |a Rationale and Objectives - Fluid-attenuated inversion recovery (FLAIR) imaging is playing an increasingly significant role in the detection of brain metastases with a concomitant increase in the number of magnetic resonance imaging (MRI) examinations. Therefore, the purpose of this study was to investigate the impact on image quality and diagnostic confidence of an innovative deep learning-based accelerated FLAIR (FLAIRDLR) sequence of the brain compared to conventional (standard) FLAIR (FLAIRS) imaging. - Materials and Methods - Seventy consecutive patients with staging cerebral MRIs were retrospectively enrolled in this single-center study. The FLAIRDLR was conducted using the same MRI acquisition parameters as the FLAIRS sequence, except for a higher acceleration factor for parallel imaging (from 2 to 4), which resulted in a shorter acquisition time of 1:39 minute instead of 2:40 minutes (−38%). Two specialized neuroradiologists evaluated the imaging datasets using a Likert scale that ranged from 1 to 4, with 4 indicating the best score for the following parameters: sharpness, lesion demarcation, artifacts, overall image quality, and diagnostic confidence. Additionally, the image preference of the readers and the interreader agreement were assessed. - Results - The average age of the patients was 63 ± 11years. FLAIRDLR exhibited significantly less image noise than FLAIRS, with P-values of< .001 and< .05, respectively. The sharpness of the images and the ability to detect lesions were rated higher in FLAIRDLR, with a median score of 4 compared to a median score of 3 in FLAIRS (P-values of<.001 for both readers). In terms of overall image quality, FLAIRDLR was rated superior to FLAIRS, with a median score of 4 vs 3 (P-values of<.001 for both readers). Both readers preferred FLAIRDLR in 68/70 cases. - Conclusion - The feasibility of deep learning FLAIR brain imaging was shown with additional 38% reduction in examination time compared to standard FLAIR imaging. Furthermore, this technique has shown improvement in image quality, noise reduction, and lesion demarcation. | ||
| 650 | 4 | |a Brain imaging | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a FLAIR | |
| 700 | 1 | |a Hauser, Till-Karsten |e VerfasserIn |4 aut | |
| 700 | 1 | |a Mengel, Annerose |e VerfasserIn |4 aut | |
| 700 | 1 | |a Brunnée, Merle |e VerfasserIn |0 (DE-588)1253346364 |0 (DE-627)1795502088 |4 aut | |
| 700 | 1 | |a Zerweck, Leonie |e VerfasserIn |4 aut | |
| 700 | 1 | |a Richter, Vivien |e VerfasserIn |4 aut | |
| 700 | 1 | |a Zuena, Mario |e VerfasserIn |4 aut | |
| 700 | 1 | |a Schuhholz, Martin |e VerfasserIn |4 aut | |
| 700 | 1 | |a Ernemann, Ulrike |e VerfasserIn |4 aut | |
| 700 | 1 | |a Gohla, Georg |e VerfasserIn |4 aut | |
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