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

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

A semi-empirical model for scatter field reduction in digital mammography

X-ray mammography is the gold standard technique in breast cancer screening programmes. One of the main challenges that mammography is still facing is scattered radiation, which degrades the quality of the image and complicates the diagnosis process. Anti-scatter grids, the main standard physical scattering reduction technique, have some unresolved challenges as they increase the dose delivered to the patient, do not remove all the scattered radiation and increase the cost of the equipment. Alternative scattering reduction methods based on post-processing algorithms, have lately been under investigation. This study is concerned with the use of image post-processing to reduce the scatter contribution in the image, by convolving the primary plus scatter image with kernels obtained from simplified Monte Carlo (MC) simulations. The proposed semi-empirical approach uses up to five thickness-dependant symmetric kernels to accurately estimate the scatter contribution of different areas of the image. Single breast thickness-dependant kernels can over-estimate the scatter signal up to 60%, while kernels adapting to local variations have to be modified for each specific case adding high computational costs. The proposed method reduces the uncertainty to a 4%-10% range for a 35-70 mm breast thickness range, making it a very efficient, case-independent scatter modelling technique. To test the robustness of the method, the scattered corrected image has been successfully compared against full MC simulations for a range of breast thicknesses. In addition, clinical images of the 010A CIRS phantom were acquired with a mammography system with and without the presence of the anti-scatter grid. The grid-less images were post-processed and their quality was compared against the grid images, by evaluating the contrast-to-noise ratio and variance ratio using several test objects, which simulate calcifications and tumour masses. The results obtained show that the method reduces the scatter to similar levels than the anti-scatter grids

A semi-empirical model for scatter field reduction in digital mammography.

X-ray mammography is the gold standard technique in breast cancer screening programmes. One of the main challenges that mammography is still facing is scattered radiation, which degrades the quality of the image and complicates the diagnosis process. Anti-scatter grids, the main standard physical scattering reduction technique, have some unresolved challenges as they increase the dose delivered to the patient, do not remove all the scattered radiation and increase the cost of the equipment. Alternative scattering reduction methods based on post-processing algorithms, have lately been under investigation. This study is concerned with the use of image post-processing to reduce the scatter contribution in the image, by convolving the primary plus scatter image with kernels obtained from simplified Monte Carlo (MC) simulations. The proposed semi-empirical approach uses up to five thickness-dependant symmetric kernels to accurately estimate the scatter contribution of different areas of the image. Single breast thickness-dependant kernels can over-estimate the scatter signal up to 60%, while kernels adapting to local variations have to be modified for each specific case adding high computational costs. The proposed method reduces the uncertainty to a 4%-10% range for a 35-70 mm breast thickness range, making it a very efficient, case-independent scatter modelling technique. To test the robustness of the method, the scattered corrected image has been successfully compared against full MC simulations for a range of breast thicknesses. In addition, clinical images of the 010A CIRS phantom were acquired with a mammography system with and without the presence of the anti-scatter grid. The grid-less images were post-processed and their quality was compared against the grid images, by evaluating the contrast-to-noise ratio and variance ratio using several test objects, which simulate calcifications and tumour masses. The results obtained show that the method reduces the scatter to similar levels than the anti-scatter grids

Design, development and use of a deformable breast phantom to assess the relationship between thickness and lesion visibility in full field digital mammography

Aim of research:This research aimed to design and develop a synthetic anthropomorphic breast phantom with cancer mimicking lesions and use this phantom to assess the relationship between lesion visibility and breast thickness in mammography. Due to the risk of cancer induction associated with the use of ionising radiation on breast tissues, experiments on human breast tissue was not practical. Therefore, a synthetic anthropomorphic breast phantom with cancer mimicking lesions was needed to be designed and developed in order to provide a safe platform to evaluate the relationship between lesion visibility and breast thickness in mammography. Method: As part of this research custom Polyvinyl alcohol (PVAL) breast phantoms with embedded PVAL lesions doped with contrast agent were fabricated and utilised. These breast phantoms exhibited mechanical and X-ray properties which were similar to female breast/breast cancer tissues. In order for this research to be useful for human studies, patient safety factors have constrained the extent of this research. These factors include compression force and radiation dose. After acquiring mammograms of phantoms with varying thicknesses, the image quality of the embedded lesions were evaluated both perceptually and mathematically.The two-alternative forced choice (2AFC) perceptual method was used to evaluate image quality of the lesions. For mathematical evaluation the following methods were utilised: line profile analysis, contrast-to noise ratio (CNR), signal-to noise ratio (SNR) and figure of merit (FOM).Results: The results of the visual perception analysis of the mammograms demonstrate that as breast compressed thickness reduces the image quality increases. Additionally, the results display a correlation in the reduction in the level of noise with the reduction in breast thickness. This noise reduction was also demonstrated in the profile plots of the lesions. The line profile analysis, in agreement with visual perception, shows improvement of sharpness of the lesion edge in relation to the reduction of the phantom thickness. The intraclass correlation coefficient (ICC) has shown a great consistency and agreement among the observers for visibility, sharpness, contrast and noise. The ICC results are not as conclusive for the size criterion. Mathematical evaluation results also show a correlation of improvement in the image quality with the reduction in breast thickness. The results show that for the measures CNR, SNR, and FOM, the increase in image quality has a threshold after which the image quality ceases to improve and instead begins to reduce. CNR and FOM dropped when the breast phantom thickness was reduced approximately 40% of its initial thickness. This consistently happened at the point where the filter changed from rhodium (Rh) to molybdenum (Mo). Conclusion: This breast phantom study successfully designed and developed an anthropomorphic compressible breast phantom with cancer mimicking lesions with mechanical and X-ray properties similar to human breast tissue. This study also demonstrates that as breast compressed thickness reduces the visibility of the perceived lesion increases. The radiation dose generally decreases up to the point that the filter changes from rhodium to molybdenum. After this point, the radiation dose increases regardless of the phantom thickness. The results from this thesis are likely to have implications for clinical practice, as they support the need for compression/thickness reduction to enhance lesion visibilit