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Status: Bibliographieeintrag

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Verfasst von:Wessling, Daniel [VerfasserIn]   i
 Gassenmaier, Sebastian [VerfasserIn]   i
 Olthof, Susann-Cathrin [VerfasserIn]   i
 Benkert, Thomas [VerfasserIn]   i
 Weiland, Elisabeth [VerfasserIn]   i
 Afat, Saif [VerfasserIn]   i
 Preibsch, Heike [VerfasserIn]   i
Titel:Novel deep-learning-based diffusion weighted imaging sequence in 1.5 T breast MRI
Verf.angabe:Daniel Wessling, Sebastian Gassenmaier, Susann-Cathrin Olthof, Thomas Benkert, Elisabeth Weiland, Saif Afat, Heike Preibsch
E-Jahr:2023
Jahr:September 2023
Umfang:9 S.
Illustrationen:Illustrationen
Fussnoten:Online verfügbar: 25. Juni 2023 ; Gesehen am 25.10.1023
Titel Quelle:Enthalten in: European journal of radiology
Ort Quelle:Amsterdam [u.a.] : Elsevier Science, 1990
Jahr Quelle:2023
Band/Heft Quelle:166(2023) vom: Sept., Artikel-ID 110948, Seite 1-9
ISSN Quelle:1872-7727
Abstract:PURPOSE: This study aimed to assess the technical feasibility, the impact on image quality, and the acquisition time (TA) of a new deep-learning-based reconstruction algorithm in diffusion weighted imaging (DWI) of breast magnetic resonance imaging (MRI). METHODS: Retrospective analysis of 55 female patients who underwent breast DWI at 1.5 T. Raw data were reconstructed using a deep-learning (DL) reconstruction algorithm on a subset of the acquired averages, therefore a reduction of TA. Clinically used standard DWI sequence (DWIStd) and the DL-reconstructed images (DWIDL) were compared. Two radiologists rated the image quality of b800 and ADC images, using a Likert-scale from 1 to 5 with 5 being considered perfect image quality. Signal intensities were measured by placing a region of interest (ROI) at the same position in both sequences. RESULTS: TA was reduced by 40 % in DWIDL, compared to DWIStd, DWIDL improved noise and sharpness while maintaining contrast, the level of artifacts, and diagnostic confidence. There were no differences regarding the signal intensity values of the apparent diffusion coefficient (ADC), (p = 0.955), b50-values (p = 0.070) and b800-values (p = 0.415) comparing standard and DL-imaging. Lesion assessment showed no differences regarding the number of lesions in ADC and DWI (both p = 1.000) and regarding the lesion diameter in DWI (p = 0.961;0.972) and ADC (p = 0.961;0.972). CONCLUSIONS: The novel deep-learning-based reconstruction algorithm significantly reduces TA in breast DWI, while improving sharpness, reducing noise, and maintaining a comparable level of image quality, artifacts, contrast, and diagnostic confidence. DWIDL does not influence the quantifiable parameters.
DOI:doi:10.1016/j.ejrad.2023.110948
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1016/j.ejrad.2023.110948
 Volltext: https://www.sciencedirect.com/science/article/pii/S0720048X23002620
 DOI: https://doi.org/10.1016/j.ejrad.2023.110948
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:artificial intelligence
 breast cancer
 deep learning
 diffusion magnetic resonance imaging
 female
 humans
 magnetic resonance imaging
 MRI
 reproducibility of results
 retrospective studies
 sexually transmitted diseases
K10plus-PPN:1867603675
Verknüpfungen:→ Zeitschrift

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