Dynamic Computed Tomography is a promising tool to investigate affections, predominantly of vascular and perivascular origin. Advances in CT technology are opening up the possibility to also evaluate musculoskeletal diseases using dynamic CT. Wide beam CT scanners have the potential to acquire images from the same anatomic area over lengths up to 16 cm. As such, detailed analysis of joint morphology and dynamic phenomena can be investigated. Based on image processing techniques, kinematics can then be obtained from these images. Motion artifacts are however a major concern in dynamic CT for musculoskeletal applications. Such artifacts are influenced by the acquisition protocol and the speed of motion. In this study, we designed a phantom that could rotate in the gantry of the CT in an attempt to quantify motion artifacts. Dynamic scans were obtained while the phantom was rotating at 720/s in the wide beam CT scanner. Visual inspection revealed two main types of artifacts: high intensity artifacts and blurring or distortion of the holes in the phantom. Two quantitative metrics were defined to assess the degree of motion artifacts induced by the rotation of the phantom. These metrics were used to compare two different dynamic CT acquisition protocols; cine and cardiac. Results showed that the cardiac protocol significantly preserved the original shape of the holes by as much as 75%±8 while that of cine was 62% ± 4. However, the cardiac protocol showed 2 times more pixels with high intensity spots than the cine protocol and that difference was significant.
In recent years, there has been an increasing interest in replacing digital subtraction angiography (DSA) as method of choice for the diagnostic imaging of patients suffering from lower extremity peripheral arterial disease (PAD). Due to small vessel diameters and suboptimal resolution, examinations of below-the-knee arteries however remain extremely challenging. The advent of wide beam CT scanners allows to perform multiple CT acquisitions over a wide patient volume. A sequence of these CT acquisitions at timed intervals could provide additional hemodynamic information, and as such allows to track a contrast bolus that propagates through the arterial conduit. The aim of this study was to evaluate the accuracy and precision of ow velocity measurements using time-resolved computed tomography angiography (CTA). To this end, we constructed a mechanical ow phantom (single lumen, 6 mm inner-diameter). Six consecutive time-resolved CTA acquisitions were performed at a constant ow rate to achieve six reference velocities (21.2 mm/s, 38.9 mm/s, 60.1 mm/s, 81.4 mm/s, 99.0 mm/s and 120.3 mm/s). The mean centerline ow velocity was obtained from the contrast propagation over three different segmental lengths (160 mm, 80 mm and 40 mm) and then compared to the reference ow velocity. The results of this study suggest that mean ow velocities within the range of typical blood ow velocities in the below-the-knee arteries (40 mm/s - 70 mm/s), can be accurately measured with high precision in a 6 mm ow phantom using time-resolved CTA when considering a minimal path length of 80 mm.
A common complication associated with hip arthoplasty is prosthesis migration, and for most cemented components a migration greater than 0.85 mm within the first six months after surgery, are an indicator for prosthesis failure. Currently, prosthesis migration is evaluated using X-ray images, which can only reliably estimate migrations larger than 5 mm. We propose an automated method for estimating prosthesis migration more accurately, using CT images and image registration techniques. We report on the results obtained using an experimental set-up, in which a metal prosthesis can be translated and rotated with respect to a cadaver femur, over distances and angles applied using a combination of positioning stages. Images are first preprocessed to reduce artefacts. Bone and prosthesis are extracted using consecutive thresholding and morphological operations. Two registrations are performed, one aligning the bones and the other aligning the prostheses. The migration is estimated as the difference between the found transformations. We use a robust, multi-resolution, stochastic optimization approach, and compare the mean squared intensity differences (MS) to mutual information (MI). 30 high-resolution helical CT scans were acquired for prosthesis translations ranging from 0.05 mm to 4 mm, and rotations ranging from 0.3° to 3° . For the translations, the mean 3D registration error was found to be 0.22 mm for MS, and 0.15 mm for MI. For the rotations, the standard deviation of the estimation error was 0.18° for MS, and 0.08° for MI. The results show that the proposed approach is feasible and that clinically acceptable accuracies can be obtained. Clinical validation studies on patient images will now be undertaken.
Image reconstruction from truncated tomographic data is an important practical problem in CT in order to reduce the X-ray dose and to improve the resolution. The main problem with the Radon Transform is that in 2D the inversion formula globally depends upon line integrals of the object function. The standard Filtered Backprojection algorithm (FBP) does not allow any type of truncation. A typical strategy is to extrapolate the truncated projections with a smooth 1D function in order to reduce the discontinuity artefacts. The low-frequency artifact reduction however, severely depends upon the width of the extrapolation, which is unknown in practice. In this paper we develop a modified ConTraSPECT-type method for specific use on truncated 2D CT-data, when only a local area (ROI) is to be imaged. The algorithm describes the shape and structure of the region surrounding the ROI by a specific object with only few parameters, in this paper a uniform ellipse. The parameters of this ellipse are optimized by minimizing the Helgason-Ludwig consistency conditions for the sinogram completed with Radon data of the ellipse. Simulations show that the MSE of the reconstructions is reduced significantly, depending on the type of truncation.
In this paper, we develop a new algorithm that enables the reconstruction of a region of interest (ROI) in X-ray Computed Tomography (CT), in case only a local region of the object is to be imaged. The method uses a Gaussian window function in order to reduce the X-ray attenuation from the region outside the ROI. The method uses almost completely local data and reduces the amount of exposure significantly. Many algorithms can be easily combined with our algorithm in order to improve the reconstruction quality. The main goal of this work is to reduce the bias in order to allow quantitative analysis of the CT-images.