| 1. |
- Andersson, Karin, 1989-
(author)
-
Metal artifacts in computed tomography : impact of reduction methods on image quality and radiotherapy treatment planning
- 2018
-
Doctoral thesis (other academic/artistic)abstract
- Degradation of image quality by metal artifacts is a common problem in computed tomography (CT) imaging, which can limit the diagnostic value of a CT examination and also introduce inaccuracies in radiotherapy (RT) treatment planning. In recent years, commercial metal artifact reduction (MAR) methods have been launched by several CT vendors. The overall aim of this thesis was to evaluate MAR methods in diagnostic imaging and RT treatment planning.Evaluations of hip prosthesis phantom CT images showed that MAR algorithms in general improved image quality, based on both visual grading analysis and quantitative measures, while the application of virtual monoenergetic reconstructions insufficiently reduced metal artifacts. In some cases additional artifacts were introduced by the MAR algorithms. MAR algorithms were also evaluated in hip prosthesis phantom CT imaging used for proton therapy treatment planning, where improvements in dose calculation accuracy were observed.Studies of Head & Neck (H&N) implant CT images in RT treatment planning were also performed. By visual grading of anatomy visualization with respect to target delineation in dental implant patient images, MAR algorithms were shown to significantly improve image quality. However, only minor effects of H&N implant artifacts on proton dose distributions were seen. The impact might be greater for more severe artifacts than those studied here, and thus further investigations of such cases are needed.In conclusion, MAR algorithms have been shown to enhance image quality for diagnostic applications and to improve anatomy visualization in RT treatment planning. The MAR algorithms led to increased proton dose calculation accuracy in some cases, while in other situations only minor changes were seen.
|
|
| 2. |
- Jendeberg, Johan, 1972-
(author)
-
Non-enhanced single-energy computed tomography of urinary stones
- 2021
-
Doctoral thesis (other academic/artistic)abstract
- Computed tomography (CT) is the mainstay imaging method for urinary stones.The aim of this thesis was to optimize the information obtained from the initial CT scan to allow a well-founded diagnosis and prognosis, and to guide the clinician as early and as far as possible in the further treatment of urinary stone disease.We examined CT scan parameters with regards to their importance for prediction of spontaneous ureteral stone passage, the impact of interreader variability of stone size estimates on this prediction, and the predictive accuracy of a semi-automated, three-dimensional (3D) segmentation algorithm. We also developed and tested the ability of a machine learning algorithm to classify pelvic calcifications into ureteral stones and phleboliths.Using single-energy CT, three quantitative methods for classification of stone composition into uric acid and non-uric acid stones in vivo were prospectively validated, using dual-energy CT as reference.Our results show that spontaneous ureteral stone passage can be predicted with high accuracy, with knowledge of stone size and position. The interreader variability in the size estimation has a large impact on the predicted outcome, but can be eliminated through a 3D segmentation algorithm. Which size estimate we use is of minor importance, but it is important that we use the chosen estimate consistently. A machine learning algorithm can differentiate distal ureteral stones from phleboliths, but more than local features are needed to reach optimal discrimination.A single-energy CT method can distinguish uric acid from non-uric acid stones in vivo with accuracy comparable to dual-energy CT.In conclusion, single-energy CT not only detects a urinary stone, but can also provide us with a prediction regarding spontaneous stone passage and a classification of stone type into uric acid and non-uric acid.
|
|
