KEYWORDS: Denoising, Radio over Fiber, Dual energy imaging, Reconstruction algorithms, Neuroimaging, Brain, X-rays, Visible radiation, Signal attenuation, Photon counting
A Dual-Energy CT (DECT) with a spectral detector greatly extends the capabilities of CT by incorporating energy-dependent information of the X-ray attenuation. In order to fully exploit DECT capabilities, it is required to perform a process known as spectral decomposition. However, this process is sensitive to noise, suffers from reduced photon count per layer in DECT scans and generates anti-correlated noise in the estimated material specific images. In order to overcome these problems, the Anti-Correlated Rudin, Osher and Fatemi (AC-ROF) model is applied for noise reduction, exploiting the relationship between the material-specific images. However, this model deteriorates the structural information with intense noise. In this paper we propose to extend this method by integrating it into an iterative reconstruction procedure to improve the noise reduction performance. The resulting algorithm is called Iterative Reconstruction AC-ROF, or IR-AC-ROF. We have tested AC-ROF and IR-AC-ROF algorithms with realistic brain simulation phantoms and show encouraging results indicating that the resulting material-specific images of IR-AC-ROF can generate better mono-energetic images with improved brain structure visibility. This demonstrates the benefit of including the noise reduction constraints within the reconstruction procedure, rather than using them in a post-processing step.
KEYWORDS: Photons, Sensors, X-rays, X-ray detectors, Monte Carlo methods, Iodine, Scattering, Absorption, Mass attenuation coefficient, Signal detection
We report on the modeling, characterization, benchmarking, and optimization of an interventional cone beam CT system based on a dual layer X-ray detector by means of physics based simulations.
By Monte Carlo methods, we log the interaction and dose deposition (i.e. signal generation) of X-ray photons in the dual layer geometry, including scattering processes and fluorescence photon emission. From the spatial dose distribution inside the detection volume, we derive typical detector properties like X-ray spectral responses, detective quantum efficiencies 𝐷𝑄𝐸(0), and noise characteristics for particular detector layouts.
We apply these results in subsequent full system simulations to generate 3D imaging scans of dual layer spectral projections, for custom virtual phantoms containing inserts of e.g. blood sediment or iodine with different concentrations. These simulated images are used to calculate key performance indicators of the imaging system, like e.g. receiver operating characteristic based analysis of material separation capabilities.
In an acute stroke parts of the human brain undergo subtle physiological changes, which are often visible as hypo- or hyperdense regions in Computed Tomography (CT) images. In the case of ischemic stroke usually an edema develops due to undersupplied cells forming regions of a core (dead tissue) and a penumbra (salvable tissue). For stroke diagnosis and outcome control it is very important to know the location and size of these different kinds of damaged tissue.
We have modelled the changes in elemental composition of brain tissue in different phases of an ischemic stroke. Influence of a number of factors on the absolute Hounsfield units is investigated as possible causes of intra- and interpatient variation. The modeled pathological changes are included in different software brain models. Subsequently we have simulated X-ray images of these brain models acquired by dual energy Cone Beam Computed Tomography (CBCT). Our modelling is based on a combination of analytical and Monte-Carlo methods. As an example of spectral processing virtual monoenergetic images are reconstructed from the simulated projections.
Simulated images are intended to optimize acquisition parameters for clinical studies beforehand and to develop new image processing algorithms to enhance the diagnostic value. As example a water map is calculated to better visualize the formation of an edema after ischemic stroke.
KEYWORDS: Sensors, Beam shaping, Monte Carlo methods, Signal detection, Optical simulations, Image filtering, Optical filters, X-rays, Signal to noise ratio, X-ray computed tomography
While cone-beam CT using flat x-ray detectors has gained increased popularity in the past years, the 3D imaging
quality is still limited by a large amount of scatter, low dynamic range, and small field of view of the detector.
Especially for large objects, the high dynamic range of the projections is a common source for detector specific
artifacts. In conventional CT, the application of beam shapers (or bowtie filters) to decrease the signal dynamic
in the projections is quite common. In this paper we investigate the use of a beam shaper for cone-beam CT
with an off-centered flat detector by means of Monte-Carlo (MC) simulations and test-bench experiments.
The shift of the detector out of the central axis increases the field of view and allows the imaging of larger
patients, but in turn leads to a very high dynamic signal range and poor scatter-to-primary ratios (SPR). The
impact of a half bowtie filter on key performance parameters of the imaging chain is investigated with MC
simulations. It is demonstrated that a beam shaper significantly improves the peak SPR especially for large
patients and that the reshaping of the SPR has a dominant impact on the homogeneity of the reconstructed image.
The use of beam shapers for CBCT requires a modified pre-processing chain that also accounts for secondary effects introduced by the beam modulation filter. Beside patient scatter correction, the inhomogeneous spectral hardening of the x-ray beam and scattered radiation from the beam shaper itself have to be corrected. A comparison of phantom scans with and without beam shaper after pre-processing demonstrates the potential of beam shapers for dose reduction and SNR improvement in flat detector cone-beam CT.
KEYWORDS: Systems modeling, Signal detection, Interference (communication), X-rays, Sensors, Denoising, X-ray detectors, Signal to noise ratio, Statistical analysis, Fourier transforms
The detective quantum efficiency (DQE) is regarded as a suitable parameter to assess the global imaging performance of an x-ray detector. However, residual signals increase the signal-to-noise ratio and therefore artificially increase the measured DQE compared to a lag-free system. In this paper, the impact of lag on the DQE is described for two different sources of lag using linear system models. In addition to the commonly used temporal filtering model for trapping, an increase of the dark current is considered as another potential source of lag. It is shown that the assumed lag model has a crucial impact on the choice of an adequate lag estimation method. Examples are given using the direct conversion material PbO. It turns out that the most general approach is the evaluation of the temporal noise power spectrum. A new algorithm is proposed for the crucial issue of robustly estimating the power spectrum at frequency zero.
A flat X-ray detector with lead oxide (PbO) as direct conversion material has been developed. The material lead oxide, which has a very high X-ray absorption, was analysed in detail including Raman spectroscopy and electron microscopy. X-ray performance data such as dark current, charge yield and temporal behaviour were evaluated on small functional samples. A process to cover a-Si TFT-plates with PbO has been developed. We present imaging results from a large
detector with an active area of 18 × 20 cm2. The detector has 1080 × 960 pixels with a pixel pitch of 184 μm. The linearity of detector response was verified. The NPS was determined with a total dark noise as low as 1800 electrons/pixel. The MTF was measured with two different methods: first with the analysis of a square wave phantom and second with a narrow slit. The MTF at the Nyquist frequency of 2.72 lp/mm was 50 %. We calculated first DQE values of our prototype detector plates. Full size images of anatomic and technical phantoms are shown.
In this paper a hierarchical spatial scalable wavelet video coder is presented. The scheme employs backward motion compensation, therefore no motion vectors have to be transmitted. The coarser levels of the wavelet decomposition of the current frame are used for motion estimation and motion compensation of the lowpass band of the next finer level. The lowpass band of the coarsest level has to be coded separately, e.g. using DPCM.
In this paper a new algorithm for joint detection and segmentation of human faces in color images sequence is presented. A skin probability image is generated using a model for skin color. Instead of a binary segmentation to detect skin regions, connected operators are used to analyze the skin probability image at different threshold levels. A hierarchical scheme of operators using shape and texture simplifies the skin probability image. For the remaining connected components, the likelihood of being a face is estimated using principal components analysis. To track a detected face region through the sequence, the connected component that represent the face in the previous frame is projected into the current frame. Using the projected segment as a marker, connected operators extract the actual face region from the skin probability image.
A fast full search block matching algorithm is developed. The matching criterion is the sum of absolute differences or the mean square error. The algorithm evaluates lower bounds for the matching criteria for subdivided blocks in order to reduce the number of search positions. It also uses the lower bounds for a fast calculation of the matching criterion for the remaining search positions. The computational complexity of the algorithm is evaluated and compared to the three-step search strategy. The search result of the algorithm is identical to the search result of the exhaustive search.
In this paper a H.263 compatible region-of-interest coding system is presented. A face detection and tracking algorithm is applied to find the region-of-interest (ROI). The input image is filtered by a region-adaptive lowpass filter which blurs the image outside the facial region. This region- adaptive lowpass filtering leads to a graceful degradation of the image quality outside the ROI, whereas the ROI remains unchanged. Since no modification is necessary at the encoder, the ROI preprocessing step is compatible to any existing implementation of the H.263 encoder. For face detection, color information is integrated into a detection algorithm based on principal components analysis. Once a face is detected, tracking is based on color information. The ROI filter can be combined with any video sequence coder. Simulation results are given using the H.263 standard. The bitrate can be significantly reduced while retaining a high perceptual quality.
A method for still image coding is proposed which allows for progressive transmission, because low detailed versions of the image can be reconstructed form a truncated bit stream. The proposed method is in its main aspects close to the classical pyramid approach of Burt and Adelson. While retaining the main idea of using a Laplacian pyramid type decomposition, the new proposal differs in the filters employed for pyramid decomposition and in the bit allocation and quantization. The image is decomposed into a centered spline Laplacian pyramid. The pyramid is quantized and coded following a layered quantization approach together with a layered coding method based on conditional arithmetic coding. The encoder outputs an embedded bit stream. Thus the decoder may truncate the bitstream at any point, which results in a more or less detailed image. Besides this rate- distortion scalability the coder has a multiresolution property, due to the pyramid decomposition. An extension to hybrid video coding is also discussed.
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