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Register a new userDetectability assessment of an x-ray imaging system using the nodes in a wavelet packet decomposition of a star-bar object
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Added by
Antonio Gonz\\'alez-L\\'opez
Publication date
2020
fields
Physics
and research's language is
English
Authors
Antonio Gonzalez-Lopez
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Purpose: Using linear transformation of the data allows studying detectability of an imaging system on a large number of signals. An appropriate transformation will produce a set of signals with different contrast and different frequency contents. In this work both strategies are explored to present a task-based test for the detectability of an x-ray imaging system. Methods: Images of a new star-bar phantom are acquired with different entrance air KERMA and with different beam qualities. Then, after a wavelet packet is applied to both input and output of the system, conventional statistical decision theory is applied to determine detectability on the different images or nodes resulting from the transformation. A non-prewhitening matching filter is applied to the data in the spatial domain, and ROC analysis is carried out in each of the nodes. Results: AUC maps resulting from the analysis present the area under the ROC curve over the whole 2D frequency space for the different doses and beam qualities. Also, AUC curves, obtained by radially averaging the AUC maps allows comparing detectability of the different techniques as a function of the frequency in one only figure. The results obtained show differences between images acquired with different doses for each of the beam qualities analyzed. Conclusions: Combining a star-bar as test object, a wavelet packet as linear transformation, and ROC analysis results in an appropriate task-based test for detectability performance of an imaging system. The test presented in this work allows quantification of system detectability as a function of the 2D frequency interval of the signal to detect. It also allows calculation of detectability differences between different acquisition techniques and beam qualities.
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A new set of signals for studying detectability of an x-ray imaging system is presented. The results obtained with these signals are intended to complement the NEQ results. The signals are generated from line spread profiles by progressively removing their lower frequency components and the resulting high frequency residues (HFRs) form the set of signals to be used in detectability studies. Detectability indexes for these HFRs are obtained using a non-prewhitening (NPW) observer and a series of edge images are used to obtain the HFRs, the covariance matrices required by the NPW model and the MTF and NPS used in NEQ calculations. The template used in the model is obtained by simulating the processes of blurring and sampling of the edge images. Comparison between detectability indexes for the HFRs and NEQ are carried out for different acquisition techniques using different beam qualities and doses. The relative sensitivity shown by detectability indexes using HFRs is higher than that of NEQ, especially at lower doses. Also, the different observers produce different results at high doses: while the ideal Bayesian observer used by NEQ distinguishes between beam qualities, the NPW used with the HFRs produces no differences between them. Delta functions used in HFR are the opposite of complex exponential functions in terms of their support in the spatial and frequency domains. Since NEQ can be interpreted as detectability of these complex exponential functions, detectability of HFRs is presented as a natural complement to NEQ in the performance assessment of an imaging system.
An improved analysis for single particle imaging (SPI) experiments, using the limited data, is presented here. Results are based on a study of bacteriophage PR772 performed at the AMO instrument at the Linac Coherent Light Source (LCLS) as part of the SPI initiative. Existing methods were modified to cope with the shortcomings of the experimental data: inaccessibility of information from the half of the detector and small fraction of single hits. General SPI analysis workflow was upgraded with the expectation-maximization based classification of diffraction patterns and mode decomposition on the final virus structure determination step. The presented processing pipeline allowed us to determine the three-dimensional structure of the bacteriophage PR772 without symmetry constraints with a spatial resolution of 6.9 nm. The obtained resolution was limited by the scattering intensity during the experiment and the relatively small number of single hits.
Background: Investigation of the functioning of the brain in living systems has been a major effort amongst scientists and medical practitioners. Amongst the various disorder of the brain, epilepsy has drawn the most attention because this disorder can affect the quality of life of a person. In this paper we have reinvestigated the EEGs for normal and epileptic patients using surrogate analysis, probability distribution function and Hurst exponent. Results: Using random shuffled surrogate analysis, we have obtained some of the nonlinear features that was obtained by Andrzejak textit{et al.} [Phys Rev E 2001, 64:061907], for the epileptic patients during seizure. Probability distribution function shows that the activity of an epileptic brain is nongaussian in nature. Hurst exponent has been shown to be useful to characterize a normal and an epileptic brain and it shows that the epileptic brain is long term anticorrelated whereas, the normal brain is more or less stochastic. Among all the techniques, used here, Hurst exponent is found very useful for characterization different cases. Conclusions: In this article, differences in characteristics for normal subjects with eyes open and closed, epileptic subjects during seizure and seizure free intervals have been shown mainly using Hurst exponent. The H shows that the brain activity of a normal man is uncorrelated in nature whereas, epileptic brain activity shows long range anticorrelation.
In this paper, we present a new method to generate an instantaneous volumetric image using a single x-ray projection. To fully extract motion information hidden in projection images, we partitioned a projection image into small patches. We utilized a sparse learning method to automatically select patches that have a high correlation with principal component analysis (PCA) coefficients of a lung motion model. A model that maps the patch intensity to the PCA coefficients is built along with the patch selection process. Based on this model, a measured projection can be used to predict the PCA coefficients, which are further used to generate a motion vector field and hence a volumetric image. We have also proposed an intensity baseline correction method based on the partitioned projection, where the first and the second moments of pixel intensities at a patch in a simulated image are matched with those in a measured image via a linear transformation. The proposed method has been valid in simulated data and real phantom data. The algorithm is able to identify patches that contain relevant motion information, e.g. diaphragm region. It is found that intensity correction step is important to remove the systematic error in the motion prediction. For the simulation case, the sparse learning model reduced prediction error for the first PCA coefficient to 5%, compared to the 10% error when sparse learning is not used. 95th percentile error for the predicted motion vector is reduced from 2.40 mm to 0.92mm. In the phantom case, the predicted tumor motion trajectory is successfully reconstructed with 0.82 mm mean vector field error compared to 1.66 mm error without using the sparse learning method. The algorithm robustness with respect to sparse level, patch size, and existence of diaphragm, as well as computation time, has also been studied.
A multi-prism lens (MPL) is a refractive x-ray lens with one-dimensional focusing properties. If used as a pre-object collimator in a scanning system for medical x-ray imaging, it reduces the divergence of the radiation and improves on photon economy compared to a slit collimator. Potential advantages include shorter acquisition times, a reduced tube loading, or improved resolution. We present the first images acquired with an MPL in a prototype for a scanning mammography system. The lens showed a gain of flux of 1.32 compared to a slit collimator at equal resolution, or a gain in resolution of 1.31-1.44 at equal flux. We expect the gain of flux in a clinical set-up with an optimized MPL and a custom-made absorption filter to reach 1.67, or 1.45-1.54 gain in resolution.
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