A comprehensive model for x-ray projection imaging system efficiency and image quality characterization in the presence of scattered radiation.

This work proposes a method for assessing the detective quantum efficiency (DQE) of radiographic imaging systems that include both the x-ray detector and the antiscatter device. Cascaded linear analysis of the antiscatter device efficiency (DQEASD) with the x-ray detector DQE is used to develop a metric of system efficiency (DQEsys); the new metric is then related to the existing system efficiency parameters of effective DQE (eDQE) and generalized DQE (gDQE). The effect of scatter on signal transfer was modelled through its point spread function (PSF), leading to an x-ray beam transfer function (BTF) that multiplies with the classical presampling modulation transfer function (MTF) to give the system MTF. Expressions are then derived for the influence of scattered radiation on signal-difference to noise ratio (SDNR) and contrast-detail (c-d) detectability. The DQEsys metric was tested using two digital mammography systems, for eight x-ray beams (four with and four without scatter), matched in terms of effective energy. The model was validated through measurements of contrast, SDNR and MTF for poly(methyl)methacrylate thicknesses covering the range of scatter fractions expected in mammography. The metric also successfully predicted changes in c-d detectability for different scatter conditions. Scatter fractions for the four beams with scatter were established with the beam stop method using an extrapolation function derived from the scatter PSF, and validated through Monte Carlo (MC) simulations. Low-frequency drop of the MTF from scatter was compared to both theory and MC calculations. DQEsys successfully quantified the influence of the grid on SDNR and accurately gave the break-even object thickness at which system efficiency was improved by the grid. The DQEsys metric is proposed as an extension of current detector characterization methods to include a performance evaluation in the presence of scattered radiation, with an antiscatter device in place

Breast Tomosynthesis: Aspects on detection and perception of simulated lesions

The aim of this thesis was to investigate aspects on detectability of simulated lesions (microcalcifications and masses) in digital mammography (DM) and breast tomosynthesis (BT). Perception in BT image volumes were also investigated by evaluating certain reading conditions. The first study concerned the effect of system noise on the detection of masses and microcalcification clusters in DM images using a free-response task. System noise has an impact on image quality and is related to the dose level. It was found to have a substantial impact on the detection of microcalcification clusters, whereas masses were relatively unaffected. The effect of superimposed tissue in DM is the major limitation hampering the detection of masses. BT is a three-dimensional technique that reduces the effect of superimposed tissue. In the following two studies visibility was quantified for both imaging modalities in terms of the required contrast at a fixed detection performance (92% correct decisions). Contrast detail plots for lesions with sizes 0.2, 1, 3, 8 and 25 mm were generated. The first study involved only an in-plane BT slice, where the lesion centre appeared. The second study repeated the same procedure in BT image volumes for 3D distributed microcalcification clusters and 8 mm masses at two dose levels. Both studies showed that BT needs substantially less contrast than DM for lesions above 1 mm. Furthermore, the contrast threshold increased as the lesion size increased for both modalities. This is in accordance with the reduced effect of superimposed tissue in BT. For 0.2 mm lesions, substantially more contrast was needed. At equal dose, DM was better than BT for 0.2 mm lesions and microcalcification clusters. Doubling the dose substantially improved the detection in BT. Thus, system noise has a substantial impact on detection. The final study evaluated reading conditions for BT image volumes. Four viewing procedures were assessed: free scroll browsing only or combined with initial cine loops at frame rates of 9, 14 and 25 fps. They were viewed on a wide screen monitor placed in vertical or horizontal positions. A free-response task and eye tracking were utilized to record the detection performance, analysis time, visual attention and search strategies. Improved reading conditions were found for horizontally aligned BT image volumes when using free scroll browsing only or combined with a cine loop at the fastest frame rate

Assessment and optimisation of digital radiography systems for clinical use

Digital imaging has long been available in radiology in the form of computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound. Initially the transition to general radiography was slow and fragmented but in the last 10-15 years in particular, huge investment by the manufacturers, greater and cheaper computing power, inexpensive digital storage and high bandwidth data transfer networks have lead to an enormous increase in the number of digital radiography systems in the UK. There are a number of competing digital radiography (DR) technologies, the most common are computer radiography (CR) systems followed by indirect digital radiography (IDR) systems. To ensure and maintain diagnostic quality and effectiveness in the radiology department appropriate methods are required to evaluate and optimise the performance of DR systems. Current semi-quantitative test object based methods routinely used to examine DR performance suffer known short comings, mainly due to the subjective nature of the test results and difficulty in maintaining a constant decision threshold among observers with time. Objective image quality based measurements of noise power spectra (NPS) and modulation transfer function (MTF) are the ‘gold standard’ for assessing image quality. Advantages these metrics afford are due to their objective nature, the comprehensive noise analysis they permit and in the fact that they have been reported to be relatively more sensitive to changes in detector performance. The advent of DR systems and access to digital image data has opened up new opportunities in applying such measurements to routine quality control and this project initially focuses on obtaining NPS and MTF results for 12 IDR systems in routine clinical use. Appropriate automatic exposure control (AEC) device calibration and a reproducible measurement method are key to optimising X-ray equipment for digital radiography. The uses of various parameters to calibrate AEC devices specifically for DR were explored in the next part of the project and calibration methods recommended. Practical advice on dosemeter selection, measurement technique and phantoms were also given. A model was developed as part of the project to simulate CNR to optimise beam quality for chest radiography with an IDR system. The values were simulated for a chest phantom and adjusted to describe the performance of the system by inputting data on phosphor sensitivity, the signal transfer function (STF), the scatter removal method and the automatic exposure control (AEC) responses. The simulated values showed good agreement with empirical data measured from images of the phantom and so provide validation of the calculation methodology. It was then possible to apply the calculation technique to imaging of tissues to investigate optimisation of exposure parameters. The behaviour of a range of imaging phosphors in terms of energy response and variation in CNR with tube potential and various filtration options were investigated. Optimum exposure factors were presented in terms of kV-mAs regulation curves and the large dose savings achieved using additional metal filters were emphasised. Optimum tube potentials for imaging a simulated lesion in patient equivalent thicknesses of water ranging from 5-40 cm thick for example were: 90-110kVp for CsI (IDR); 80-100kVp for Gd2O2S (screen /film); and 65-85kVp for BaFBrI. Plots of CNR values allowed useful conclusions regarding the expected clinical operation of the various DR phosphors. For example 80-90 kVp was appropriate for maintaining image quality over an entire chest radiograph in CR whereas higher tube potentials of 100-110 kVp were indicated for the CsI IDR system. Better image quality is achievable for pelvic radiographs at lower tube potentials for the majority of detectors however, for gadolinium oxysulphide 70-80 kVp gives the best image quality. The relative phosphor sensitivity and energy response with tube potential were also calculated for a range of DR phosphors. Caesium iodide image receptors were significantly more sensitive than the other systems. The percentage relative sensitivities of the image receptors averaged over the diagnostic kV range were used to provide a method of indicating what the likely clinically operational dose levels would be, for example results suggested 1.8 µGy for CsI (IDR); 2.8 µGy for Gd2O2S (Screen/film); and 3.8 µGy for BaFBrI (CR). The efficiency of scatter reduction methods for DR using a range of grids and air gaps were also reviewed. The performance of various scatter reduction methods: 17/70; 15/80; 8/40 Pb grids and 15 cm and 20 cm air gaps were evaluated in terms of the improvement in CNR they afford, using two different models. The first, simpler model assumed quantum noise only and a photon counting detector. The second model incorporated quantum noise and system noise for a specific CsI detector and assumed the detector was energy integrating. Both models allowed the same general conclusions and suggest improved performance for air gaps over grids for medium to low scatter factors and both models suggest the best choice of grid for digital systems is the 15/80 grid, achieving comparable or better performance than air gaps for high scatter factors. The development, analysis and discussion of AEC calibration, CNR value, phosphor energy response, and scatter reduction methods are then brought together to form a practical step by step recipe that may be followed to optimise digital technology for clinical use. Finally, CNR results suggest the addition of 0.2 mm of copper filtration will have a negligible effect on image quality in DR. A comprehensive study examining the effect of copper filtration on image quality was performed using receiver operator characteristic (ROC) methodology to include observer performance in the analysis. A total of 3,600 observations from 80 radiographs and 3 observers were analysed to provide a confidence interval of 95% in detecting differences in image quality. There was no statistical difference found when 0.2 mm copper filtration was used and the benefit of the dose saving promote it as a valuable optimisation tool

The use of a figure-of-merit (FOM) for optimization in digital mammography: an exploratory study in Malta

This PhD thesis comprises an exploratory study in digital mammography physics that portrays two essential components. The first component (1) presents the first national survey of the technical performance of mammography equipment in Malta using the European Protocol [1-3]. This demonstrated considerable differences in the technical performance of the mammography units across the country with a wide range in performance, patient dose and image quality. A common problem was that many clinics had implemented computed radiography (CR) systems to replace existing film-screen (FS) systems without due consideration to optimization. All direct digital (DR) mammography units met current international technical performance standards and the effectiveness of DR mammography in reducing patient dose and maintaining high image quality compared to CR has been confirmed. The second component (2) was to explore the use of a figure-of-merit (FOM) for optimization and characterisation in digital mammography. The use of image quality parameters in digital mammography such as contrast-to-noise ratio (CNR) or signal-difference-to-noise ratio (SDNR), signal-to-noise ratio (SNR) and detective quantum efficiency (DQE) have been traditionally used for the quantitative evaluation of the system performance against international standards or guidelines. The use of FOMs is relatively new and may be considered as a new quality assurance tool in digital mammography permitting the quantitative and simultaneous assessment of image quality and patient dose. The main objective in having a FOM is to have a numerical expression representing the efficiency and efficacy of a given system gauging how good or poor a system is performing. This may be useful in optimization and in predicting a predetermined or expected image quality with a given amount of radiation dose for a given system. The most interesting aspect of the FOMs in this work will be to investigate and explore the possibility for inter-system comparison

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..