In computed tomography, star shape artifacts are introduced by metal objects, which are inside a patient's
body. The quality of the reconstructed image can be enhanced by applying a metal artifact reduction method. Unfortunately, a method that removes all such artifacts in order to make the images valuable for medical diagnosis remains to be found. In this study, the influence of metal segmentation is investigated. A thresholding technique, which is the state of the art in the field, is compared with a manual segmentation. Results indicate that a more accurate segmentation can lead to a preservation of important anatomical details, which are of high value for medical diagnosis.
In computed tomography (CT), the nonlinear characteristics of beam hardening are due to the polychromaticity of X-rays, which severely degrade the CT image quality and diagnostic accuracy. The correction of beam hardening has been an active area since the early years of CT, and various techniques have been developed. State of-the-art works on multi-material beam hardening correction (BHC) are mainly based on segmenting datasets into different materials, and correcting the non-linearity iteratively. Those techniques are limited in correction effectiveness due to inaccurate segmentation. Furthermore, most of them are computationally intensive. In this study, we introduce a fast BHC scheme based on frequency splitting with the fact that beam hardening artifacts mainly contain in the low frequency components and take more iterations to be corrected in comparison with high frequency components. After low-pass filtering and correcting artifacts at down-sampled projections, an artifact reduced high resolution reconstruction will be obtained by incorporating the original edge information from the high frequency components. Evaluations in terms of correction accuracy and computational efficiency are performed using simulated and real CT datasets. In comparison to the BHC algorithm without frequency splitting, the proposed accelerated algorithm yields comparable results in correcting cupping and streak artifacts with tremendously reduced computational effort. We conclude that the presented framework can achieve a significant speedup while still obtaining excellent artifact reduction. This is a significant practical advantage for clinical as well as industrial CT.
Temporal resolution is an important issue especially in cardiac CT. To quantify it, often merely the time that is
needed to acquire rawdata that contribute to a reconstructed image is used. In combination with more complex
reconstruction algorithms, which aim to improve the temporal resolution, (e.g. TRI-PICCS) this procedure
has proven to be inadequate. This study proposes and evaluates a more accurate simulation-based technique to
assess the temporal resolution of a CT system (including its reconstruction algorithm). To calculate the temporal
resolution of the system on a single point within the field of measurement, a vessel which performs a cardiac
motion pattern is simulated at this position. The motion pattern is adapted such that the accuracy loss caused
by motion exactly meets a defined threshold and then the temporal resolution can be taken from that motion
pattern. Additionally the dependency of the temporal resolution on the direction of the motion is evaluated to
obtain a measure of the reliability. The method is applied to single source and dual source full scan and short
scan reconstructions as well as on TRI-PICCS reconstructions. The results give an accurate impression on the
system response to motion. In conclusion, the proposed method allows quantifying the temporal resolution of a
CT system as a function of many parameters (motion direction, source position, object position, reconstruction
algorithm).
Dual energy CT (DECT) provides material-selective CT images by acquiring the object of interest with two
different x-ray spectra, a low and a high energy spectrum. Today, two techniques to process the rawdata are in
use: Image-based DECT reconstructs the low and the high energy data separately and then performs a linear
combination of the images to yield the desired material-selective images. This method can only provide a first
order approximation of the true material decomposition and it will not be able to remove higher order beam
hardening artifacts from the images. By contrast, rawdata-based DECT naturally deals with higher order effects
and is therefore the better way to go. However, rawdata-based DECT requires the same line integrals to be
available for both scans (consistent scans). This requirement may not be met for CT scanners that are available
today. To handle the material decomposition of DECT from inconsistent scans (i.e. non-overlapping rays for
each measured spectrum) a material decomposition algorithm (MDIR) that allows for different scan trajectories
and scan geometries for the low and the high energy scan was developed and evaluated. The results of our
iterative algorithm are comparable to those obtained by a rawdata-based approach. However, conventional
rawdata-based approaches are often not applicable since inconsistent rays are acquired. It should be noted that
MDIR can be extended to scans with more than two different spectra and to decompositions into more than two
basis functions in a straightforward way.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.