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| Home > Publications Database > Deep Learning-Based Signal Amplification of T1-Weighted Single-Dose Images Improves Metastasis Detection in Brain MRI. |
| Journal Article | DZNE-2025-00778 |
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2025
Lippincott Williams & Wilkins
Philadelphia, Pa.
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Please use a persistent id in citations: doi:10.1097/RLI.0000000000001166
Abstract: 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.
Keyword(s): Humans (MeSH) ; Deep Learning (MeSH) ; Brain Neoplasms: diagnostic imaging (MeSH) ; Brain Neoplasms: secondary (MeSH) ; Female (MeSH) ; Middle Aged (MeSH) ; Male (MeSH) ; Magnetic Resonance Imaging: methods (MeSH) ; Prospective Studies (MeSH) ; Contrast Media: administration & dosage (MeSH) ; Sensitivity and Specificity (MeSH) ; Aged (MeSH) ; Image Interpretation, Computer-Assisted: methods (MeSH) ; Image Enhancement: methods (MeSH) ; artificial double-dose ; brain metastasis ; contrast maximization ; convolutional neural network ; deep learning ; gadolinium-based contrast agent ; magnetic resonance imaging ; metastasis detection ; virtual contrast ; Contrast Media
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