Digital radiography: image acquisition and scattering reduction in x-ray imaging.

Abstract

Since the discovery of the X-rays in 1895, their use in both medical and industrial imaging applications has gained increasing importance. As a consequence, X-ray imaging devices have evolved and adapted to the needs of individual applications, leading to the appearance of digital image capture devices. Digital technologies introduced the possibility of separating the image acquisition and image processing steps, allowing their individual optimization. This thesis explores both areas, by seeking the improvement in the design of the new family of Varex Imaging CMOS X-ray detectors and by developing a method to reduce the scatter contribution in mammography examinations using image post-processing techniques. During the CMOS X-ray detector product design phase, it is crucial to detect any short- comings that the detector might present. Image characterization techniques are a very efficient method for finding these possible detector features. This first part of the thesis focused in taking these well-known test methods and adapt and optimize them, so they could act as a red flag indicating when something needed to be investigated. The methods chosen in this study have proven to be very effective in finding detector short- comings and the designs have been optimised in accordance with the results obtained. With the aid of the developed imaging characterization tests, new sensor designs have been successfully integrated into a detector, resulting in the recent release into the market of a new family of Varex Imaging CMOS X-ray detectors. The second part of the thesis focuses in X-ray mammography applications, the gold standard technique in breast cancer screening programmes. Scattered radiation degrades the quality of the image and complicates the diagnosis process. Anti-scatter grids, the main scattering reduction technique, are not a perfect solution. This study is concerned with the use of image post-processing to reduce the scatter contribution in the image, by convolving the output image with kernels obtained from simplified Monte Carlo simulations. The proposed semi-empirical approach uses three thickness-dependant symmetric kernels to accurately estimate the environment contribution to the breast, which has been found to be of key importance in the correction of the breast-edge area. When using a single breast thickness-dependant kernel to convolve the image, the post-processing technique can over-estimate the scattering up to 60%. The method presented in this study reduces the uncertainty to a 4-10% range for a 35 to 70 mm breast thickness range, making it a very efficient scatter modelling technique. The method has been successfully proven against full Monte Carlo simulations and mammography phantoms, where it shows clear improvements in terms of the contrast to noise ratio and variance ratio when the performance is compared against images acquired with anti-scatter grids.