title: Data-driven Measures for Dose Reduction and Image Quality Enhancement in Computed Tomography
creator: Byl, Achim Jan
subject: ddc-530
subject: 530 Physics
description: Dose reduction without sacrificing image quality is one of the primary aims of computed  tomography (CT) research. Especially in interventional applications, which require multiple  scans, dose reduction is paramount. Two pathways of arbitrarily reducing dose are the reduction  of tube current (low-mAs CT) and reduction of number of projections (sparse-view CT). The  former will increase noise, the latter sparseness artifacts, such that there is a trade-off between  dose and image quality in either case. Additionally, if the patient does not fully fit in the  field of measurement, as is typical for interventional cone-beam CT, the image will suffer from  truncation artifacts and a small field of view. Novel deep neural networks have shown promising  results in a variety of image processing tasks. The aim of this thesis is therefore to analyze the  different low-dose CT realizations in conjunction with deep learning-based image processing and  facilitate reconstruction from truncated projections with both a data-driven and iterative method.  Quantitative image quality analysis of low-dose CT is performed with several conventional and  task-based methods. The latter are able to distinguish between sufficiently and insufficiently  trained networks, ensuring a safe utilization of deep learning-based methods. The well-trained  neural networks are able to support the tested 80% dose reduction by restoring image quality.  Between the different realizations of low-dose CT, low-mAs CT is determined as preferable.  Both detruncation methods achieve satisfactory results. However, the computational cost of  DART remains prohibitive while the deep learning-based detruncation promises to increase the  field of view in real-time. This in turn may improve image guidance and secondary algorithms  during operations.
date: 2024
type: Dissertation
type: info:eu-repo/semantics/doctoralThesis
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/34983/1/2024%20Diss%20Data-Driven%20Dose%20Reduction%20and%20Image%20Quality%20Enhancements%20Achim%20Byl.pdf
identifier: DOI:10.11588/heidok.00034983
identifier: urn:nbn:de:bsz:16-heidok-349833
identifier:   Byl, Achim Jan  (2024) Data-driven Measures for Dose Reduction and Image Quality Enhancement in Computed Tomography.  [Dissertation]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/34983/
rights: info:eu-repo/semantics/openAccess
rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html
language: eng