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

Análisis y propuesta de métricas de calidad de imagen médica que mimetizan al observador humano

La investigación que se presenta en este documento se centra en el paradigma de la percepción automática de la calidad de imagen médica, y en la correlación de dicha percepción con la percepción humana. El análisis de la calidad de imagen médica tiene un lugar central en el diseño de sistemas de imagen para diagnóstico. El objetico de este análisis es, usualmente, el de diseñar una métrica capaz de evaluar la calidad de imagen percibida por un observador, una IQM por sus siglas en inglés (Image Quality Metric). Más aún, el objetivo de un gran número de investigadores es el de desarrollar métricas automatizadas capaces de reproducir los resultados que produciría un observador humano ante dichas imágenes. De forma prácticamente universal, estas métricas se desarrollan como programas informáticos, desarrollados en uno u otro lenguaje de programación. Hasta el momento solo se han obtenido éxitos parciales. El número existente de aproximaciones a este problema y, por tanto, el número de algoritmos desarrollado es elevado; sin embargo, sigue siendo una cuestión abierta. En la literatura médica se encuentran dos aproximaciones claramente diferenciadas; una de ellas está basada en modelos de la función visual humana o en modelos ideales de observador (bien juntos o por separado). Estos modelos tratan de reproducir el procesado de la imagen en el observador desde su captación en el ojo hasta su procesado de alto nivel en el cerebro. Son modelos muy complejos, con una validez limitada y no han mostrado respuestas satisfactorias y, sobre todo, generalizables. Son estudios y modelos típicos en el campo de la imagen médica. Por otro lado, los especialistas del mundo de las Telecomunicaciones han analizado la calidad de imagen desde un punto de vista más amplio, más enfocado en estudios de imágenes naturales (aquellas presentes en el entorno natural humano), y tanto en estudios de imagen fija como en vídeo. Muchos de estos análisis están basados en aproximaciones “top‐down” al sistema visual humano. Estos modelos proponen hipótesis de carácter general acerca del funcionamiento del sistema visual humano y construyen modelos del mismo basándose en dichas hipótesis. Algunos de estos estudios han propuesto y desarrollado métricas muy bien correlacionadas con la percepción humana. Es quizá sorprendente que, hasta hace unos años, ha habido muy pocos estudios sobre la aplicación de estas métricas al campo de la imagen médica. Dentro de este acercamiento, la métrica que ha tenido más éxito ha sido, sin ningún género de dudas, SSIM, presentada por Wang, Bovik y Simoncelli en el año 2004. Esta métrica se basa en la teoría propuesta por Wang y Bovik sobre el funcionamiento del sistema visual humano. Esta teoría afirma que nuestro sistema visual está especialmente adaptado para extraer información estructural de una imagen. Es una aproximación en la que se parte de una teoría del funcionamiento general del sistema visual humano, en lugar de deducir un esquema de funcionamiento a partir de sus elementos funcionales. A partir de esta métrica se ha desarrollado una amplia familia de índices que comparte la estructura básica con SSIM y que ha obtenido correlaciones crecientes entre los resultados de dichas métricas y los resultados del observador humano. Actualmente es la métrica más usada para medir la calidad de imagen percibida en la industria del vídeo por cable y por satélite..

Correlation of clinical and physical-technical image quality in chest CT : a human cadaver study applied on iterative reconstruction

Background: The first aim of this study was to evaluate the correlation between clinical and physical-technical image quality applied to different strengths of iterative reconstruction in chest CT images using Thiel cadaver acquisitions and Catphan images. The second aim was to determine the potential dose reduction of iterative reconstruction compared to conventional filtered back projection based on different clinical and physical-technical image quality parameters. Methods: Clinical image quality was assessed using three Thiel embalmed human cadavers. A Catphan phantom was used to assess physical-technical image quality parameters such as noise, contrast-detail and contrast-to-noise ratio (CNR). Both Catphan and chest Thiel CT images were acquired on a multislice CT scanner at 120 kVp and 0.9 pitch. Six different refmAs settings were applied (12, 30, 60, 90, 120 and 150refmAs) and each scan was reconstructed using filtered back projection (FBP) and iterative reconstruction (SAFIRE) algorithms (1,3 and 5 strengths) using a sharp kernel, resulting in 24 image series. Four radiologists assessed the clinical image quality, using a visual grading analysis (VGA) technique based on the European Quality Criteria for Chest CT. Results: Correlation coefficients between clinical and physical-technical image quality varied from 0.88 to 0.92, depending on the selected physical-technical parameter. Depending on the strength of SAFIRE, the potential dose reduction based on noise, CNR and the inverse image quality figure (IQF(inv)) varied from 14.0 to 67.8 %, 16.0 to 71.5 % and 22.7 to 50.6 % respectively. Potential dose reduction based on clinical image quality varied from 27 to 37.4 %, depending on the strength of SAFIRE. Conclusion: Our results demonstrate that noise assessments in a uniform phantom overestimate the potential dose reduction for the SAFIRE IR algorithm. Since the IQF(inv) based dose reduction is quite consistent with the clinical based dose reduction, an optimised contrast-detail phantom could improve the use of contrast-detail analysis for image quality assessment in chest CT imaging. In conclusion, one should be cautious to evaluate the performance of CT equipment taking into account only physical-technical parameters as noise and CNR, as this might give an incomplete representation of the actual clinical image quality performance

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