Navigation überspringen
Universitätsbibliothek Heidelberg
Status: Bibliographieeintrag

Verfügbarkeit
Standort: ---
Exemplare: ---
heiBIB
 Online-Ressource
Verfasst von:Liu, Chang [VerfasserIn]   i
 Klein, Laura [VerfasserIn]   i
 Huang, Yixing [VerfasserIn]   i
 Baader, Edith [VerfasserIn]   i
 Lell, Michael [VerfasserIn]   i
 Kachelrieß, Marc [VerfasserIn]   i
 Maier, Andreas [VerfasserIn]   i
Titel:Two-view topogram-based anatomy-guided CT reconstruction for prospective risk minimization
Verf.angabe:Chang Liu, Laura Klein, Yixing Huang, Edith Baader, Michael Lell, Marc Kachelrieß & Andreas Maier
E-Jahr:2024
Jahr:23 April 2024
Umfang:11 S.
Illustrationen:Illustrationen
Fussnoten:Gesehen am 05.02.2025
Titel Quelle:Enthalten in: Scientific reports
Ort Quelle:[London] : Springer Nature, 2011
Jahr Quelle:2024
Band/Heft Quelle:14(2024), Artikel-ID 9373, Seite 1-11
ISSN Quelle:2045-2322
Abstract:To facilitate a prospective estimation of the effective dose of an CT scan prior to the actual scanning in order to use sophisticated patient risk minimizing methods, a prospective spatial dose estimation and the known anatomical structures are required. To this end, a CT reconstruction method is required to reconstruct CT volumes from as few projections as possible, i.e. by using the topograms, with anatomical structures as correct as possible. In this work, an optimized CT reconstruction model based on a generative adversarial network (GAN) is proposed. The GAN is trained to reconstruct 3D volumes from an anterior-posterior and a lateral CT projection. To enhance anatomical structures, a pre-trained organ segmentation network and the 3D perceptual loss are applied during the training phase, so that the model can then generate both organ-enhanced CT volume and organ segmentation masks. The proposed method can reconstruct CT volumes with PSNR of 26.49, RMSE of 196.17, and SSIM of 0.64, compared to 26.21, 201.55 and 0.63 using the baseline method. In terms of the anatomical structure, the proposed method effectively enhances the organ shapes and boundaries and allows for a straight-forward identification of the relevant anatomical structures. We note that conventional reconstruction metrics fail to indicate the enhancement of anatomical structures. In addition to such metrics, the evaluation is expanded with assessing the organ segmentation performance. The average organ dice of the proposed method is 0.71 compared with 0.63 for the baseline model, indicating the enhancement of anatomical structures.
DOI:doi:10.1038/s41598-024-59731-y
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.

kostenfrei: Volltext: https://doi.org/10.1038/s41598-024-59731-y
 DOI: https://doi.org/10.1038/s41598-024-59731-y
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Humans
 Prospective Studies
 Phantoms, Imaging
 Radiation Dosage
 Imaging, Three-Dimensional
 Algorithms
 Tomography, X-Ray Computed
 Image Processing, Computer-Assisted
 CT
 Two-view reconstruction
K10plus-PPN:1916432174
Verknüpfungen:→ Zeitschrift

Permanenter Link auf diesen Titel (bookmarkfähig):  https://katalog.ub.uni-heidelberg.de/titel/69301096   QR-Code
zum Seitenanfang