Inflammatory white matter brain lesions are a key pathological finding in patients suffering from multiple sclerosis (MS). Image based quantification of different characteristics of these lesions has become an elemental bio-marker in both diagnosis as well as therapy monitoring during treatment of these patients. Whilst it has been shown that the lesion load at a single point in time is only of limited value with respect to explaining clinical symptoms of the patients, a more robust estimate of disease activity can be observed by analyzing the evolution of lesions over time. Here, we propose a system for automated monitoring of temporal lesion evolution in MS. We describe an approach for analysis of lesion correspondence, along with a pipeline for fully automated computation of this model. The pipeline consists of a U-Net based lesion segmentation, a non-linear image registration between multiple studies, computation of temporal lesion correspondences, and finally an analysis module for extracting and visualizing quantitative parameters from the model.
KEYWORDS: Modulation transfer functions, Sensors, X-ray computed tomography, X-rays, Point spread functions, X-ray detectors, Signal detection, Signal to noise ratio, Image quality, Visibility
In Computed Tomography (CT), the image quality sensitively depends on the accuracy of the X-ray projection signal, which is acquired by a two-dimensional array of pixel cells in the detector. If the signal of X-ray photons is spread out to neighboring pixels (crosstalk), a decrease of spatial resolution may result. Moreover, streak and ring artifacts may emerge. Deploying system simulations for state-of-the-art CT detector configurations, we characterize origin and appearance of these artifacts in the reconstructed CT images for different scenarios. A uniform pixel-to-pixel crosstalk results in a loss of spatial resolution only. The Modulation Transfer Function (MTF) is attenuated, without affecting the limiting resolution, which is defined as the first zero of the MTF. Additional streak and ring artifacts appear, if the pixel-to-pixel crosstalk is non-uniform. Parallel to the system simulations we developed an analytical model. The model explains resolution loss and artifact level using the first and second derivative of the X-ray profile acquired by the detector. Simulations and analytical model are in agreement to each other. We discuss the perceptibility of ring and streak artifacts within noisy images if no crosstalk correction is applied.
High-density objects, such as metal prostheses or surgical clips, generate streak-like artifacts in CT images. We designed a radial adaptive filter, which directly operates on the corrupted reconstructed image, to effectively and efficiently reduce such artifacts. The filter adapts to the severity of local artifacts to preserve spatial resolution as much as possible. The widths and direction of the filter are derived from the local structure tensor. Visual inspection shows that this novel radial adaptive filter is superior with respect to existing methods in the case of mildly distorted images. In the presence of strong artifacts we propose a hybrid approach. An image corrected with a standard method, which performs well on images with regions of severe artifacts, is fused with an adaptively filtered clone to combine the strengths of both methods.
Image artifacts caused by a temporally delayed response of a back illuminated photodiode deployed in a CT detector were studied. The temporal response pattern, characterized by a finite rise and fall time, is an intrinsic property of a photodiode. Generally, electron-hole pairs generated in the diode take time to diffuse to contacts
where they get finally registered. In the case of backilluminated diodes, diffusion time is significantly prolonged, since photons hit the diode on the back. Electrons or holes only contribute to the signal, if they travel the full distance to the frontend, where contacts are located. To study the temporal behavior of a back illuminated photodiode a computer model for a standard third generation CT scanner was devised and simulations were carried out. Resulting image artifacts were quantified for various phantom and photodiode parameters. Simulations and theory demonstrate that for a given phantom, artifacts scale with rise/fall time and the angular speed of the scanner.
KEYWORDS: Metals, Digital filtering, Computed tomography, Tissues, Image segmentation, Modulation transfer functions, Signal to noise ratio, Image filtering, Data modeling, Bone
In CT imaging, high absorbing objects such as metal bodies may cause significant artifacts, which may, for example, result in dose inaccuracies in the radiation therapy planning process. In this work, we aim at reducing the local and global image artifact, in order to improve the overall dose accuracy. The key part f this approach is the correction of the original projection data in those regions, which feature defects caused by rays traversing the high attenuating objects in the patient. The affected regions are substituted by model data derived from the original tomogram deploying a segmentation method. Phantom and climnical studies demonstrate that the proposed method significantly reduces the overall artifacts while preserving the information content of the image as much as possible. The image quality improvements were quantified by determining the signal-to-noise ratio, the artifact level and the modulation transfer function. The proposed method is computationally efficient and can easily be integrated into commercial CT scanners and radiation therapy planning software.
A novel CT detector based on CMOS photodiodes has been developed. A detector module comprises two identical photosensor arrays mounted to a ceramic substrate. Each sensor has a matrix of 20 by 10 pixels. Pixels are 1 mm (channel direction) x 1.8 mm (slice) large and consist of a photodiode, charge integration unit and a sample
and hold stage. An automated switching between a low and a high sensitivity mode allows for a dynamic range of 17 bits. The integrated signals are read out, transferred to a printed circuit board (at a rate of 2463 Hz per pixel) and here converted into a digital data stream. The structured cadmium tungstate scintillator features lead stripes between pixels to reduce x-ray crosstalk and to shield the underlying in-pixel electronics. During assembling care was taken to ensure that the lead stripes of the scintillator entirely cover the pixel electronics underneath. Several prototype modules have been assembled and their performance concerning linearity, noise, crosstalk, and temperature dependence has been evaluated.
X-ray scattering in megavoltage portal imaging becomes more of an issue when quantitative results are needed. This is the case in megavoltage computed tomography (MVCT) and transit dosimetry, where the absorbed dose delivered to the patient is to be reconstructed. Although sensor arrays based on amorphous silicon (a-Si) photodiodes show promising results for this application, the scatter problem has so far not been examined. In this paper portal scatter distributions are calculated by means of Monte-Carlo (MC) simulations for typical clinical parameters. The aim of the MC simulations is to design a detector which is able to reject photons and electrons scattered by the phantom. As expected the analysis of the spectrum shows that multiply scattered photons can be differentiated from singly scattered photons by means of their energy. The MC results indicate that by using a detector with a high-Z conversion plate combined with a moderately thick phosphor screen a significant fraction of low energy scattered photons and most electrons can be rejected. However, to reduce the scatter signal further a software correction method based on a dedicated scatter model is still necessary.
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