An X-ray system with a large area detector has high scatter-to-primary ratios (SPRs), which result in severe artifacts in reconstructed computed tomography (CT) images. A scatter correction algorithm is introduced that provides effective scatter correction but does not require additional patient exposure. The key hypothesis of the algorithm is that the high-frequency components of the X-ray spatial distribution do not result in strong high-frequency signals in the scatter. A calibration sheet with a checkerboard pattern of semitransparent blockers (a "primary modulator") is inserted between the X-ray source and the object. The primary distribution is partially modulated by a high-frequency function, while the scatter distribution still has dominant low-frequency components, based on the hypothesis. Filtering and demodulation techniques suffice to extract the low-frequency components of the primary and hence obtain the scatter estimation. The hypothesis was validated using Monte Carlo (MC) simulation, and the algorithm was evaluated by both MC simulations and physical experiments. Reconstructions of a software humanoid phantom suggested system parameters in the physical implementation and showed that the proposed method reduced the relative mean square error of the reconstructed image in the central region of interest from 74.2% to below 1%. In preliminary physical experiments on the standard evaluation phantom, this error was reduced from 31.8% to 2.3%, and it was also demonstrated that the algorithm has no noticeable impact on the resolution of the reconstructed image in spite of the filter-based approach. Although the proposed scatter correction technique was implemented for X-ray CT, it can also be used in other X-ray imaging applications, as long as a primary modulator can be inserted between the X-ray source and the imaged object.
Deconvolution-based analysis of CT and MR brain perfusion data is widely used in clinical practice and it is still a topic of ongoing research activities. In this paper, we present a comprehensive derivation and explanation of the underlying physiological model for intravascular tracer systems. We also discuss practical details that are needed to properly implement algorithms for perfusion analysis. Our description of the practical computer implementation is focused on the most frequently employed algebraic deconvolution methods based on the singular value decomposition. In particular, we further discuss the need for regularization in order to obtain physiologically reasonable results. We include an overview of relevant preprocessing steps and provide numerous references to the literature. We cover both CT and MR brain perfusion imaging in this paper because they share many common aspects. The combination of both the theoretical as well as the practical aspects of perfusion analysis explicitly emphasizes the simplifications to the underlying physiological model that are necessary in order to apply it to measured data acquired with current CT and MR scanners.
We assess dose and image quality of a state-of-the-art angiographic C-arm system (Axiom Artis dTA, Siemens Medical Solutions, Forchheim, Germany) for three-dimensional neuro-imaging at various dose levels and tube voltages and an associated measurement method. Unlike conventional CT, the beam length covers the entire phantom, hence, the concept of computed tomography dose index (CTDI) is not the metric of choice, and one can revert to conventional dosimetry methods by directly measuring the dose at various points using a small ion chamber. This method allows us to define and compute a new dose metric that is appropriate for a direct comparison with the familiar CTDIw of conventional CT. A perception study involving the CATPHAN 600 indicates that one can expect to see at least the 9 mm inset with 0.5% nominal contrast at the recommended head-scan dose (60 mGy) when using tube voltages ranging from 70 kVp to 125 kVp. When analyzing the impact of tube voltage on image quality at a fixed dose, we found that lower tube voltages gave improved low contrast detectability for small-diameter objects. The relationships between kVp, image noise, dose, and contrast perception are discussed.
X-ray image intensifiers (XRIIs) have many applications in diagnostic imaging including acquisition of near-real-time projection images of the intracranial and coronary vasculature. Recently, there has been some interest in using this projection data to generate three-dimensional (3-D) computed tomographic (CT) reconstructions. The XRII and x-ray tube are rotated around the object, acquiring sufficient data for the simultaneous reconstruction of many transverse slices. Three-dimensional reconstructions are compromised, however, if the projection data is geometrically distorted in any way. Previous studies have shown the distortion in XRIIs to be substantial and to be highly angular dependent. In this paper, we present a global correction technique which provides a table of correction coefficients for an image acquired at any arbitrary angle about the patient. The coefficients are generated using a linear least-squares fit between the detected and known locations of a grid of small steel beads which is attached to the XRII (27 cm nominal diameter). We have performed corrections on 100 images obtained during rotation of the gantry through 200 degrees and find that a fifth-order polynomial provides optimum image distortion reduction (mean residual distortion of 0.07 pixels), however, fourth-order polynomials provide sufficient distortion reduction for our application (mean residual displacement of 0.1 pixels). Using sixth-order polynomials does not provide a statistically significant reduction in image distortion. The spatial distribution of residual distortion did not demonstrate any particular pattern over the face of the XRII. Image angle and coefficient angle must be known to within +/- 2 degrees in order to keep the mean residual distortion be approximately 0.5 pixels.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.