Bier B (2019)
Publication Language: English
Publication Type: Thesis
Publication year: 2019
URI: https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/13723
Medical imaging is essential for the assessment of osteoarthritis and the overall
knee health. For that purpose, radiographs of the knees of standing patients are
acquired commonly. These suffer, however, under projective transformation and
thus do not allow conclusions to be drawn about the complex 3-D joint anatomy.
Conversely, compared to many 3-D capable imaging modalities, imaging under load is
easily feasible using X-rays. This is beneficial, since it has been shown that this reflects
the knee joint under more realistic conditions. Recently, a 3-D imaging protocol has
been proposed that enables cone-beam computed tomography (CBCT) reconstruction
of the knees acquired under weight-bearing conditions. To this end, the C-arm rotates
on a horizontal trajectory around the standing patient. Involuntary patient motion
and scattered radiation deteriorate the reconstructions’ image quality substantially. In
this thesis, novel concepts and methods are proposed to further develop this imaging
protocol in order to improve the reconstructions.
In a first approach, a primary modulator-based scatter correction method has been
transferred on a clinical C-arm CBCT system. The method is a suitable candidate
to be applied to projection images of the knees, since it is capable of estimating
heterogeneous scatter distributions. To this end, extensions to an existing method
have been developed to compensate for the system wobble and the automatic exposure
control of the C-arm systems. In multiple experiments, it is demonstrated that the
primary modulator method works on clinical C-arm scanners and also for imaging
under load.
A current state-of-the-art motion estimation method is based on metallic fiducial
markers that are placed on the patient’s skin. The marker placement is, however, a
tedious and time-consuming process. To this end, two marker-free alternatives are
proposed. In a first attempt, a range camera is utilized to track the patient surface
simultaneous to a CBCT image acquisition. Using point cloud registration of the
acquired depth frames, transformations can be computed that correspond to the
patient motion, which directly can be integrated into the image reconstruction. In a
simulation study, comparable results to the marker-based method could be achieved.
Yet, initial real data experiments on a clinical scanner did not achieve satisfying
image quality, even though part of the motion could be estimated. Therefore, the
promising results make this method to a pre-cursor to future research. Although this
method is marker-free, a prepared environment is required. Hence, another purely
image-based motion estimated approach has been investigated. The idea is to replace
the positions of the fiducial markers with the ones of anatomical landmarks present
in the projections. Anatomical landmark detection in X-ray images from different
directions is difficult and, to the best of our knowledge, has not been investigated yet.
For this purpose, a novel deep learning-based approach has been developed. In a first
evaluation, the method was tested on X-ray images of the pelvis. Here, it could be
demonstrated that the detection accuracy sufficed to initialize a 2-D/3-D registration.
Subsequently, the approach is transferred to knee projection images, where the good
detection results served as input for the motion estimation. Despite limited results on
real data acquisition, the achieved improvements of the image quality are an indicator
for a successful future application for motion estimation.
APA:
Bier, B. (2019). C-arm Cone-Beam Computed Tomography Reconstruction for Knee Imaging under Weight-Bearing Conditions (Dissertation).
MLA:
Bier, Bastian. C-arm Cone-Beam Computed Tomography Reconstruction for Knee Imaging under Weight-Bearing Conditions. Dissertation, 2019.
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