Bradford Scholars is the University of Bradford online research archive. Access is free to anyone interested in research being conducted at Bradford. In the repository you will find a range of materials from journal articles and conference papers to research reports and theses.
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Dolomite study for in situ CO 2 capture for chemical looping reforming(2015)The non-isothermal kinetic and thermal behaviour of a naturally formed dolomite in conditions that approach in situ CO2 capture in chemical looping reforming, were investigated. The performance of this dolomite was studied at micro-scale in ‘dry’ conditions, as well as at macro-scale in ‘dry’ and ‘wet’ conditions to investigate the effects of scale (3 mg, 2.5 g), partial pressures of CO2 (<15 kPa) and steam, and deactivation upon limited cycling. The carbonation and calcination kinetics were modelled using an improved iterative Coats–Redfern method. Increasing CO2 partial pressures on the ‘dry’ macroscale exacerbated the experimental carbonation conversions in an inversely proportional trend when compared with those at micro-scale. The presence of steam had a positive effect on CO2 chemisorption. Steam had a negligible influence on the calcination activation energies. The activation energies of carbonation were increased for the experiments at the highest CO2 partial pressures under wet conditions.
How much image noise can be added in cardiac x-ray imaging without loss in perceived image quality?(2015-10)Cardiologists use x-ray image sequences of the moving heart acquired in real-time to diagnose and treat cardiac patients. The amount of radiation used is proportional to image quality; however, exposure to radiation is damaging to patients and personnel. The amount by which radiation dose can be reduced without compromising patient care was determined. For five patient image sequences, increments of computer-generated quantum noise (white + colored) were added to the images, frame by frame using pixel-to-pixel addition, to simulate corresponding increments of dose reduction. The noise adding software was calibrated for settings used in cardiac procedures, and validated using standard objective and subjective image quality measurements. The degraded images were viewed next to corresponding original (not degraded) images in a two-alternativeforced- choice staircase psychophysics experiment. Seven cardiologists and five radiographers selected their preferred image based on visualization of the coronary arteries. The point of subjective equality, i.e., level of degradation where the observer could not perceive a difference between the original and degraded images, was calculated; for all patients the median was 33% 15% dose reduction. This demonstrates that a 33% 15% increase in image noise is feasible without being perceived, indicating potential for 33% 15% dose reduction without compromising patient care.
Selecting stimuli parameters for video quality studies based on perceptual similarity distances(2015-03)This work presents a methodology to optimize the selection of multiple parameter levels of an image acquisition, degradation, or post-processing process applied to stimuli intended to be used in a subjective image or video quality assessment (QA) study. It is known that processing parameters (e.g. compression bit-rate) or techni- cal quality measures (e.g. peak signal-to-noise ratio, PSNR) are often non-linearly related to human quality judgment, and the model of either relationship may not be known in advance. Using these approaches to select parameter levels may lead to an inaccurate estimate of the relationship between the parameter and subjective quality judgments – the system’s quality model. To overcome this, we propose a method for modeling the rela- tionship between parameter levels and perceived quality distances using a paired comparison parameter selection procedure in which subjects judge the perceived similarity in quality. Our goal is to enable the selection of evenly sampled parameter levels within the considered quality range for use in a subjective QA study. This approach is tested on two applications: (1) selection of compression levels for laparoscopic surgery video QA study, and (2) selection of dose levels for an interventional X-ray QA study. Subjective scores, obtained from the follow-up single stimulus QA experiments conducted with expert subjects who evaluated the selected bit-rates and dose levels, were roughly equidistant in the perceptual quality space - as intended. These results suggest that a similarity judgment task can help select parameter values corresponding to desired subjective quality levels.
Machine vision image quality measurement in cardiac x-ray imaging(2015-03)The purpose of this work is to report on a machine vision approach for the automated measurement of x-ray image contrast of coronary arteries lled with iodine contrast media during interventional cardiac procedures. A machine vision algorithm was developed that creates a binary mask of the principal vessels of the coronary artery tree by thresholding a standard deviation map of the direction image of the cardiac scene derived using a Frangi lter. Using the mask, average contrast is calculated by tting a Gaussian model to the greyscale pro le orthogonal to the vessel centre line at a number of points along the vessel. The algorithm was applied to sections of single image frames from 30 left and 30 right coronary artery image sequences from di erent patients. Manual measurements of average contrast were also performed on the same images. A Bland-Altman analysis indicates good agreement between the two methods with 95% con dence intervals -0.046 to +0.048 with a mean bias of 0.001. The machine vision algorithm has the potential of providing real-time context sensitive information so that radiographic imaging control parameters could be adjusted on the basis of clinically relevant image content.
Context sensitive cardiac x-ray imaging: a machine vision approach to x-ray dose control(2015-09)Modern cardiac x-ray imaging systems regulate their radiation output based on the thickness of the patient to maintain an acceptable signal at the input of the x-ray detector. This approach does not account for the context of the examination or the content of the image displayed. We have developed a machine vision algorithm that detects iodine-filled blood vessels and fits an idealized vessel model with the key parameters of contrast, diameter, and linear attenuation coefficient. The spatio-temporal distribution of the linear attenuation coefficient samples, when appropriately arranged, can be described by a simple linear relationship, despite the complexity of scene information. The algorithm was tested on static anthropomorphic chest phantom images under different radiographic factors and 60 dynamic clinical image sequences. It was found to be robust and sensitive to changes in vessel contrast resulting from variations in system parameters. The machine vision algorithm has the potential of extracting real-time context sensitive information that may be used for augmenting existing dose control strategies.