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| 001 | 274031 | ||
| 005 | 20250411100000.0 | ||
| 024 | 7 | _ | |a 10.1097/RLI.0000000000001107 |2 doi |
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| 024 | 7 | _ | |a 1536-0210 |2 ISSN |
| 037 | _ | _ | |a DZNE-2025-00012 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Haase, Robert |0 P:(DE-2719)9001860 |b 0 |
| 245 | _ | _ | |a Artificial T1-Weighted Postcontrast Brain MRI: A Deep Learning Method for Contrast Signal Extraction. |
| 260 | _ | _ | |a [Erscheinungsort nicht ermittelbar] |c 2025 |b Ovid |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1744358340_16420 |2 PUB:(DE-HGF) |
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| 520 | _ | _ | |a Reducing gadolinium-based contrast agents to lower costs, the environmental impact of gadolinium-containing wastewater, and patient exposure is still an unresolved issue. Published methods have never been compared. The purpose of this study was to compare the performance of 2 reimplemented state-of-the-art deep learning methods (settings A and B) and a proposed method for contrast signal extraction (setting C) to synthesize artificial T1-weighted full-dose images from corresponding noncontrast and low-dose images.In this prospective study, 213 participants received magnetic resonance imaging of the brain between August and October 2021 including low-dose (0.02 mmol/kg) and full-dose images (0.1 mmol/kg). Fifty participants were randomly set aside as test set before training (mean age ± SD, 52.6 ± 15.3 years; 30 men). Artificial and true full-dose images were compared using a reader-based study. Two readers noted all false-positive lesions and scored the overall interchangeability in regard to the clinical conclusion. Using a 5-point Likert scale (0 being the worst), they scored the contrast enhancement of each lesion and its conformity to the respective reference in the true image.The average counts of false-positives per participant were 0.33 ± 0.93, 0.07 ± 0.33, and 0.05 ± 0.22 for settings A-C, respectively. Setting C showed a significantly higher proportion of scans scored as fully or mostly interchangeable (70/100) than settings A (40/100, P < 0.001) and B (57/100, P < 0.001), and generated the smallest mean enhancement reduction of scored lesions (-0.50 ± 0.55) compared with the true images (setting A: -1.10 ± 0.98; setting B: -0.91 ± 0.67, both P < 0.001). The average scores of conformity of the lesion were 1.75 ± 1.07, 2.19 ± 1.04, and 2.48 ± 0.91 for settings A-C, respectively, with significant differences among all settings (all P < 0.001).The proposed method for contrast signal extraction showed significant improvements in synthesizing postcontrast images. A relevant proportion of images showing inadequate interchangeability with the reference remains at this dosage. |
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| 650 | _ | 7 | |a Contrast Media |2 NLM Chemicals |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a Deep Learning |2 MeSH |
| 650 | _ | 2 | |a Male |2 MeSH |
| 650 | _ | 2 | |a Contrast Media |2 MeSH |
| 650 | _ | 2 | |a Female |2 MeSH |
| 650 | _ | 2 | |a Prospective Studies |2 MeSH |
| 650 | _ | 2 | |a Magnetic Resonance Imaging: methods |2 MeSH |
| 650 | _ | 2 | |a Middle Aged |2 MeSH |
| 650 | _ | 2 | |a Brain: diagnostic imaging |2 MeSH |
| 650 | _ | 2 | |a Adult |2 MeSH |
| 700 | 1 | _ | |a Pinetz, Thomas |b 1 |
| 700 | 1 | _ | |a Kobler, Erich |b 2 |
| 700 | 1 | _ | |a Bendella, Zeynep |0 P:(DE-2719)9003165 |b 3 |u dzne |
| 700 | 1 | _ | |a Gronemann, Christian |b 4 |
| 700 | 1 | _ | |a Paech, Daniel |0 P:(DE-2719)9001705 |b 5 |u dzne |
| 700 | 1 | _ | |a Radbruch, Alexander |0 P:(DE-2719)9001861 |b 6 |u dzne |
| 700 | 1 | _ | |a Effland, Alexander |0 P:(DE-2719)9002732 |b 7 |u dzne |
| 700 | 1 | _ | |a Deike-Hofmann, Katerina |0 P:(DE-2719)9001745 |b 8 |e Last author |
| 773 | _ | _ | |a 10.1097/RLI.0000000000001107 |g Vol. 60, no. 2 |0 PERI:(DE-600)2041543-6 |n 2 |p 105 - 113 |t Investigative radiology |v 60 |y 2025 |x 0020-9996 |
| 856 | 4 | _ | |u https://pub.dzne.de/record/274031/files/DZNE-2025-00012%20SUP.doc |
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| 856 | 4 | _ | |u https://pub.dzne.de/record/274031/files/DZNE-2025-00012%20SUP.pdf |
| 856 | 4 | _ | |u https://pub.dzne.de/record/274031/files/DZNE-2025-00012_Restricted.pdf |
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