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) metal objects in the region of interest introduce data inconsistencies during acquisition.
The reconstruction process results in an image with star shaped artifacts. To enhance image quality the influence of
metal objects can be reduced by different metal artifact reduction (MAR) strategies. For an adequate evaluation of new
MAR approaches a ground truth reference data set is needed. In technical evaluations, where phantoms are available
with and without metal inserts, ground truth data can easily be acquired by a reference scan. Obviously, this is not
possible for clinical data.
In this work, three different evaluation methods for metal artifacts as well as comparison of MAR methods without the
need of an acquired reference data set will be presented and compared. The first metric is based on image contrast; a
second approach involves the filtered gradient information of the image, and the third method uses a forward projection
of the reconstructed image followed by a comparison with the actually measured projection data.
All evaluation techniques are performed on phantom and on clinical CT data with and without MAR and compared with
reference-based evaluation methods as well as expert-based classifications.
Renal lesion detection and characterization using Computed Tomography is an important application in genitourinary
radiology. Although in general the detection of renal lesions has been shown to be exceedingly accuratce, the detection of
benign renal cysts is still problematic. Under certain circumstances, the attenuation values inside a cyst increase
incorrectly with an increase in the iodine concentration in the surrounding soft tissue. This so called pseudoenhancement
complicates the classification of cysts and creates severe difficulties to distinguish a benign nonenhancing lesion from an
enhancing mass.
In the present study, the standard procedure based on a single energy 120 kV mode is compared to three dual energy
modes available on the Siemens Somatom Definition Flash scanner.
In order to simulate the kidney and the lesions, several plastic rods were placed inside a small container filled with
different iodine concentrations. This phantom is then positioned inside water tanks of different sizes. The rods simulating
the lesions are made out of a special plastic with constant HU value throughout the relevant X-ray energy range.
During the project, three important aspects have been discovered: 1) for normal situations, a 100/140 Sn kV mode on the
Siemens Flash scanner is similar to the traditional single energy 120 kV mode. 2) For small patient sizes, all dual energy
modes show a reduction of pseudoenhancement. 3) For larger patients, only the 100/140 Sn kV mode results in a
reduction of pseudoenhancement. Both the 80/140 kV and the 80/140 Sn kV mode show a worse performance than the
120 kV single energy mode in a very large phantom size.
Under normal circumstances the quality of images reconstructed with the classic FBP CT reconstruction algorithm is adequate for medical diagnosis. However, in some special cases the assumptions made by this method are not applicable because of non-linearities in the underlying physical imaging processes. Especially in the presence of metal implants in the field of view, effects like beam hardening, scatter and photon starvation result in serious streaking and banding artifacts around and between these objects. In order to reduce the artifacts, several different types of correction methods were introduced during the last two decades. In one of the most often used approaches, an interpolation scheme is used to replace all corrupted beam data in the shadow of the metal with artificially generated values. Although this leads to a reduction of the most severe artifacts, typically the results are far from being perfect. Instead of removing all artifacts, in most cases new streak artifacts are introduced. In the present work it is shown that the origin of these new artifacts is related to the loss of edge information of the objects by using surrogate data. The application of a more sophisticated artifact reduction method based on a segmentation of a preliminary reconstructed image decreases the number of newly introduced artifacts to a large degree. This is possible, because edge information between air and tissue recovered from the preliminary reconstruction can be included into the correction scheme. It is concluded that a restoration scheme without additionally information is not sufficient for a successful metal artifact reduction method.
In this work different surrogate data strategies to reduce metal artifacts in reconstructed CT images are tested.
Inconsistent sinogram projection data caused by e.g. beam hardening are the origin of metal artifacts in the
reconstructed images. The goal of this work is to replace this inconsistent projection data by artificially generated data.
Therefore, here, two 1D interpolation strategies, a directional interpolation based upon the sinogram 'flow' and a 1D
interpolation by means of the non-equispaced fast Fourier transform are compared to a fully 2D method based upon the
idea of image inpainting. Due to the fact that the artificially generated data never perfectly fit the gap inside the
projection data caused by the inconsistencies, those repaired sinogram data are reconstructed using a weighted
Maximum Likelihood Expectation Maximization algorithm called λ-MLEM algorithm. In this way, the artificially
generated data, still contaminated with residual inconsistencies, are weighted less during reconstruction.
The collimation of strongly diverging laser beams emitted by diode lasers is performed with aspherical micro-optical components. In order to obtain a good beam profile high-quality micro-lenses with a large numerical aperture compared to conventional lenses have to be applied. The characterization of these components using conventional interferometric techniques is not suitable, costly or inaccurate with respect to the required accuracy of the lens shape. Digital Holography as a measurement tool for the characterization of micro-optical components offers several advantageous properties with respect to other interferometric techniques, such as avoidance of aberrations introduced by imaging and magnification optics. The large numerical aperture of the microlenses under test leads to high fringe densities in the holograms which can not be resolved by CCD-detectors. In order to avoid this problem digital holography is combined with multiple wavelength and speckle techniques. A diffusing screen is placed directly behind the microlens in order to destroy the large divergence and at least two measurements with different wavelengths are performed for the recovery of the wavefront information. The speckle pattern in the numerical reconstruction of the wavefront reduces the accuracy of the resulting difference phase significantly. In this paper a technique for the reduction of speckle noise is proposed which is not based on classical filtering techniques such as median filters. Several holograms of the same object under test are recorded with different speckle patterns. A proper averaging taking into account the properties of the wrapped phases leads to a improvement of the accuracy up to 1/60 of the wavelength. Results of the characterization of aspherical microlenses using the new technique are presented.
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