BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Yixing Huang (FAU Erlangen-Nürnberg) DTSTART:20210622T101500Z DTEND:20210622T114500Z DTSTAMP:20260423T022602Z UID:MathDeep/10 DESCRIPTION:Title: Deep Learning for Computed Tomography Image Reconstruction from Insuffic ient Data\nby Yixing Huang (FAU Erlangen-Nürnberg) as part of Mathema tics of Deep Learning\n\n\nAbstract\nComputed tomography (CT) image recons truction from insufficient data is a severely ill-posed inverse problem. C onventional methods solely have very limited performance to address this p roblem. Deep learning has achieved impressive results in solving various i nverse problems. \nHowever\, the robustness of deep learning methods is st ill a concern for clinical applications due to the following two challenge s: a) With limited access to sufficient training data\, a learned deep lea rning model may not generalize well to unseen data\; b) Deep learning mode ls are sensitive to noise. Therefore\, the quality of images processed by neural networks only may be inadequate. In this talk\, we investigate the robustness of deep learning in CT image reconstruction first. Since learni ng-based images with incorrect structures are likely not consistent with m easured projection data\, we propose a data consistent reconstruction (DCR ) method to improve their image quality\, which combines the advantages of conventional methods and deep learning: \nFirst\, a prior image is genera ted by deep learning. Afterwards\, unmeasured data are inpainted by forwar d projection of the prior image. \nFinally\, a final image is reconstructe d by a conventional method\, integrating data consistency for measured dat a and learned prior information for missing data. The DCR method is demons trated in two\nscenarios: image reconstruction from limited-angle data and truncated data.\n LOCATION:https://researchseminars.org/talk/MathDeep/10/ END:VEVENT END:VCALENDAR