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000274031 1001_ $$0P:(DE-2719)9001860$$aHaase, Robert$$b0
000274031 245__ $$aArtificial T1-Weighted Postcontrast Brain MRI: A Deep Learning Method for Contrast Signal Extraction.
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000274031 520__ $$aReducing 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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000274031 650_7 $$2NLM Chemicals$$aContrast Media
000274031 650_2 $$2MeSH$$aHumans
000274031 650_2 $$2MeSH$$aDeep Learning
000274031 650_2 $$2MeSH$$aMale
000274031 650_2 $$2MeSH$$aContrast Media
000274031 650_2 $$2MeSH$$aFemale
000274031 650_2 $$2MeSH$$aProspective Studies
000274031 650_2 $$2MeSH$$aMagnetic Resonance Imaging: methods
000274031 650_2 $$2MeSH$$aMiddle Aged
000274031 650_2 $$2MeSH$$aBrain: diagnostic imaging
000274031 650_2 $$2MeSH$$aAdult
000274031 7001_ $$aPinetz, Thomas$$b1
000274031 7001_ $$aKobler, Erich$$b2
000274031 7001_ $$0P:(DE-2719)9003165$$aBendella, Zeynep$$b3$$udzne
000274031 7001_ $$aGronemann, Christian$$b4
000274031 7001_ $$0P:(DE-2719)9001705$$aPaech, Daniel$$b5$$udzne
000274031 7001_ $$0P:(DE-2719)9001861$$aRadbruch, Alexander$$b6$$udzne
000274031 7001_ $$0P:(DE-2719)9002732$$aEffland, Alexander$$b7$$udzne
000274031 7001_ $$0P:(DE-2719)9001745$$aDeike-Hofmann, Katerina$$b8$$eLast author
000274031 773__ $$0PERI:(DE-600)2041543-6$$a10.1097/RLI.0000000000001107$$gVol. 60, no. 2$$n2$$p105 - 113$$tInvestigative radiology$$v60$$x0020-9996$$y2025
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