Maier J (2021)
Publication Language: English
Publication Type: Thesis
Publication year: 2021
URI: https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/17559
Weight-bearing C-arm cone-beam computed tomography (CBCT) of the knees is an imaging technique that can be applied to acquire information about the structures of the knee joint under natural loading conditions. An analysis of the knee joints under load can provide valuable information about the disease progression of patients suffering from osteoarthritis (OA). OA is a degenerative disease that causes breakdown of soft tissues like articular cartilage leading to severe pain especially during movement. Weight-bearing CBCT is beneficial compared with standing 2-D radiographs traditionally applied for OA diagnosis, as it provides 3-D information about the structures of the knees. However, this acquisition technique also poses some challenges which are addressed in this thesis, including detector saturation, motion artifacts owing to postural sway during the scan, and the analysis of the resulting reconstructions. During weight-bearing CBCT acquisition, the C-arm rotates on a horizontal trajectory around the knees. The high X-ray dose required in lateral views to penetrate through both femurs leads to detector saturation in less dense tissue regions. To address this issue, an approach for preventing detector saturation in the analog domain is presented. It non-linearly transforms the intensities using an analog tone mapping operator (TMO) before digitization in order to increase the dynamic range of the detector. Furthermore, a second approach is described that replaces saturated regions in the projection images with information obtained from an additional lowdose scan. A marker-based 3-D non-rigid alignment step makes the approach robust to subject motion between scans. Both approaches lead to improved image quality in simulations, and a clinical evaluation confirms the feasibility of the second approach. The swaying motion of naturally standing subjects during weight-bearing CBCT acquisition leads to blurring and streaking artifacts in the reconstructions. To correct these artifacts, an inertial measurement unit (IMU)-based rigid and non-rigid motion compensation is developed and evaluated in a simulation study using the recorded motion of real standing subjects. The approaches lead to improved image quality on optimal simulations. A noisy signal simulation reveals the limitations of the approach towards an application in real acquisitions. A subsequent phantom study shows that additional motion is induced by the vibration of the C-arm during the scan, which can not be measured by the IMU attached to the legs of the scanned subject. Afterwards, the thickness analysis of tibial cartilage is addressed. First, an analysis of manual segmentations of the tibial cartilage surface of multiple raters is performed showing that low-pass filtered single-rater segmentations are more similar to the consensus of multiple raters than the original segmentations. Furthermore, as a fast and repeatable alternative to manual segmentations, an automatic convolutional neural network (CNN)-based approach for cartilage surface segmentation is developed. As there is no standard measure for cartilage thickness in literature, the results of four cartilage thickness measures are compared revealing their similarities and differences. A subsequent evaluation of the change in cartilage thickness over time supports the expectation that lateral cartilage thickness decreases under load. This thesis provides valuable tools for the pipeline aimed at analyzing cartilage in OA. The presented methods contribute to the improvement of data acquisition and processing in weight-bearing CBCT and pave the road for the evaluation of clinical data through a detailed and thorough analysis of all described processing steps.
APA:
Maier, J. (2021). Methods for Weight-bearing C-arm Cone-Beam Computed Tomography of the Knees (Dissertation).
MLA:
Maier, Jennifer. Methods for Weight-bearing C-arm Cone-Beam Computed Tomography of the Knees. Dissertation, 2021.
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