Home > Publications Database > Deep Learning-Based Signal Amplification of T1-Weighted Single-Dose Images Improves Metastasis Detection in Brain MRI. > print

001     279447
005     20250715101041.0
024 7 _ |a 10.1097/RLI.0000000000001166
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024 7 _ |a 1536-0210
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037 _ _ |a DZNE-2025-00778
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Haase, Robert
|0 P:(DE-2719)9001860
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245 _ _ |a Deep Learning-Based Signal Amplification of T1-Weighted Single-Dose Images Improves Metastasis Detection in Brain MRI.
260 _ _ |a Philadelphia, Pa.
|c 2025
|b Lippincott Williams & Wilkins
336 7 _ |a article
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336 7 _ |a ARTICLE
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336 7 _ |a Journal Article
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520 _ _ |a Double-dose contrast-enhanced brain imaging improves tumor delineation and detection of occult metastases but is limited by concerns about gadolinium-based contrast agents' effects on patients and the environment. The purpose of this study was to test the benefit of a deep learning-based contrast signal amplification in true single-dose T1-weighted (T-SD) images creating artificial double-dose (A-DD) images for metastasis detection in brain magnetic resonance imaging.In this prospective, multicenter study, a deep learning-based method originally trained on noncontrast, low-dose, and T-SD brain images was applied to T-SD images of 30 participants (mean age ± SD, 58.5 ± 11.8 years; 23 women) acquired externally between November 2022 and June 2023. Four readers with different levels of experience independently reviewed T-SD and A-DD images for metastases with 4 weeks between readings. A reference reader reviewed additionally acquired true double-dose images to determine any metastases present. Performances were compared using Mid-p McNemar tests for sensitivity and Wilcoxon signed rank tests for false-positive findings.All readers found more metastases using A-DD images. The 2 experienced neuroradiologists achieved the same level of sensitivity using T-SD images (62 of 91 metastases, 68.1%). While the increase in sensitivity using A-DD images was only descriptive for 1 of them (A-DD: 65 of 91 metastases, +3.3%, P = 0.424), the second neuroradiologist benefited significantly with a sensitivity increase of 12.1% (73 of 91 metastases, P = 0.008). The 2 less experienced readers (1 resident and 1 fellow) both found significantly more metastases on A-DD images (resident, T-SD: 61.5%, A-DD: 68.1%, P = 0.039; fellow, T-SD: 58.2%, A-DD: 70.3%, P = 0.008). They were therefore able to use A-DD images to increase their sensitivity to the neuroradiologists' initial level on regular T-SD images. False-positive findings did not differ significantly between sequences. However, readers showed descriptively more false-positive findings on A-DD images. The benefit in sensitivity particularly applied to metastases ≤5 mm (5.7%-17.3% increase in sensitivity).A-DD images can improve the detectability of brain metastases without a significant loss of precision and could therefore represent a potentially valuable addition to regular single-dose brain imaging.
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588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: pub.dzne.de
650 _ 7 |a artificial double-dose
|2 Other
650 _ 7 |a brain metastasis
|2 Other
650 _ 7 |a contrast maximization
|2 Other
650 _ 7 |a convolutional neural network
|2 Other
650 _ 7 |a deep learning
|2 Other
650 _ 7 |a gadolinium-based contrast agent
|2 Other
650 _ 7 |a magnetic resonance imaging
|2 Other
650 _ 7 |a metastasis detection
|2 Other
650 _ 7 |a virtual contrast
|2 Other
650 _ 7 |a Contrast Media
|2 NLM Chemicals
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Deep Learning
|2 MeSH
650 _ 2 |a Brain Neoplasms: diagnostic imaging
|2 MeSH
650 _ 2 |a Brain Neoplasms: secondary
|2 MeSH
650 _ 2 |a Female
|2 MeSH
650 _ 2 |a Middle Aged
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a Magnetic Resonance Imaging: methods
|2 MeSH
650 _ 2 |a Prospective Studies
|2 MeSH
650 _ 2 |a Contrast Media: administration & dosage
|2 MeSH
650 _ 2 |a Sensitivity and Specificity
|2 MeSH
650 _ 2 |a Aged
|2 MeSH
650 _ 2 |a Image Interpretation, Computer-Assisted: methods
|2 MeSH
650 _ 2 |a Image Enhancement: methods
|2 MeSH
700 1 _ |a Pinetz, Thomas
|b 1
700 1 _ |a Kobler, Erich
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700 1 _ |a Bendella, Zeynep
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700 1 _ |a Zülow, Stefan
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700 1 _ |a Schievelkamp, Arndt-Hendrik
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700 1 _ |a Schmeel, Frederic Carsten
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700 1 _ |a Panahabadi, Sarah
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700 1 _ |a Stylianou, Anna Magdalena
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700 1 _ |a Paech, Daniel
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700 1 _ |a Foltyn-Dumitru, Martha
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700 1 _ |a Wagner, Verena
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700 1 _ |a Schlamp, Kai
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700 1 _ |a Heussel, Gudula
|b 13
700 1 _ |a Holtkamp, Mathias
|b 14
700 1 _ |a Heussel, Claus Peter
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700 1 _ |a Vahlensieck, Martin
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700 1 _ |a Luetkens, Julian A
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700 1 _ |a Schlemmer, Heinz-Peter
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700 1 _ |a Haubold, Johannes
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700 1 _ |a Radbruch, Alexander
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700 1 _ |a Effland, Alexander
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700 1 _ |a Deuschl, Cornelius
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700 1 _ |a Deike-Hofmann, Katerina
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773 _ _ |a 10.1097/RLI.0000000000001166
|g Vol. 60, no. 8, p. 543 - 551
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