Mammographic image restoration using maximum entropy deconvolution

An image restoration approach based on a Bayesian maximum entropy method (MEM) has been applied to a radiological image deconvolution problem, that of reduction of geometric blurring in magnification mammography. The aim of the work is to demonstrate an improvement in image spatial resolution in realistic noisy radiological images with no associated penalty in terms of reduction in the signal-to-noise ratio perceived by the observer. Images of the TORMAM mammographic image quality phantom were recorded using the standard magnification settings of 1.8 magnification/fine focus and also at 1.8 magnification/broad focus and 3.0 magnification/fine focus; the latter two arrangements would normally give rise to unacceptable geometric blurring. Measured point-spread functions were used in conjunction with the MEM image processing to de-blur these images. The results are presented as comparative images of phantom test features and as observer scores for the raw and processed images. Visualization of high resolution features and the total image scores for the test phantom were improved by the application of the MEM processing. It is argued that this successful demonstration of image de-blurring in noisy radiological images offers the possibility of weakening the link between focal spot size and geometric blurring in radiology, thus opening up new approaches to system optimization.Comment: 18 pages, 10 figure

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

Apodized-Aperture Pixel Design X-Ray Detector for Improvement of Detective Quantum Efficiency at High Spatial Frequencies

The detective quantum efficiency (DQE) is a characteristic of x-ray imaging systems describing how well a system can produce high signal-to-noise ratio images compared to an ideal detector. In medical radiography, increases in DQE result directly in increases in image SNR for a given x-ray exposure, and improved SNR has been shown to improve breast cancer detection rates in screening programs. Typically, modern x-ray detectors have DQE values about 0.6 to 0.7 at low spatial frequencies and 0.2 to 0.3 or less at high spatial frequencies. We describe a method to improve the high frequency DQE by developing a novel apodized-aperture pixel (AAP) design that can be implemented with detectors having very small elements. We show theoretically that the high-frequency DQE can be doubled using this approach. Experimental validation shows an increase from 0.2 to 0.4 at the sampling cut-off frequency (2.5 cycles/mm) for a laboratory CMOS/CsI detector. It is predicted the high-frequency DQE of a Se-based detector for mammography could be increased from 0.35 to 0.7. Such increases would improve visualization of small objects and fine detail in x-ray imaging by a factor of two

A Method to Measure the Detective Quantum Efficiency of Radiographic Systems in Clinical Setting

The risks associated with exposure to radiation make it critical that digital imaging systems give the best possible images for a given dose to the patient. The DQE is the most widely accepted measure of performance and dose efficiency for digital radiography systems, however it is not commonly measured in a clinical environment as part of routine quality assurance. The primary reason for this is that the data provided to the user by clinical systems has typically undergone image processing and therefore may have a non-linear characteristic response. This is a problem because the Fourier metrics of the DQE require a linear response. In this thesis a method to measure the DQE using processed image data is presented. The method is based on a novel theoretical approach to the linearization of the system characteristic response. The method was validated on a cesium iodide-based digital radiography at panel detector and the results where within a few percent of a conventional DQE measurement

Management of pediatric radiation dose using GE fluoroscopic equipment

In this article, we present GE Healthcare’s design philosophy and implementation of X-ray imaging systems with dose management for pediatric patients, as embodied in its current radiography and fluoroscopy and interventional cardiovascular X-ray product offerings. First, we present a basic framework of image quality and dose in the context of a cost–benefit trade-off, with the development of the concept of imaging dose efficiency. A set of key metrics of image quality and dose efficiency is presented, including X-ray source efficiency, detector quantum efficiency (DQE), detector dynamic range, and temporal response, with an explanation of the clinical relevance of each. Second, we present design methods for automatically selecting optimal X-ray technique parameters (kVp, mA, pulse width, and spectral filtration) in real time for various clinical applications. These methods are based on an optimization scheme where patient skin dose is minimized for a target desired image contrast-to-noise ratio. Operator display of skin dose and Dose-Area Product (DAP) is covered, as well. Third, system controls and predefined protocols available to the operator are explained in the context of dose management and the need to meet varying clinical procedure imaging demands. For example, fluoroscopic dose rate is adjustable over a range of 20:1 to adapt to different procedure requirements. Fourth, we discuss the impact of image processing techniques upon dose minimization. In particular, two such techniques, dynamic range compression through adaptive multiband spectral filtering and fluoroscopic noise reduction, are explored in some detail. Fifth, we review a list of system dose-reduction features, including automatic spectral filtration, virtual collimation, variable-rate pulsed fluoroscopic, grid and no-grid techniques, and fluoroscopic loop replay with store. In addition, we describe a new feature that automatically minimizes the patient-to-detector distance, along with an estimate of its dose reduction potential. Finally, two recently developed imaging techniques and their potential effect on dose utilization are discussed. Specifically, we discuss the dose benefits of rotational angiography and low frame rate imaging with advanced image processing in lieu of higher-dose digital subtraction

A GPU Simulation Tool for Training and Optimisation in 2D Digital X-Ray Imaging

Conventional radiology is performed by means of digital detectors, with various types of technology and different performance in terms of efficiency and image quality. Following the arrival of a new digital detector in a radiology department, all the staff involved should adapt the procedure parameters to the properties of the detector, in order to achieve an optimal result in terms of correct diagnostic information and minimum radiation risks for the patient. The aim of this study was to develop and validate a software capable of simulating a digital X-ray imaging system, using graphics processing unit computing. All radiological image components were implemented in this application: an X-ray tube with primary beam, a virtual patient, noise, scatter radiation, a grid and a digital detector. Three different digital detectors (two digital radiography and a computed radiography systems) were implemented. In order to validate the software, we carried out a quantitative comparison of geometrical and anthropomorphic phantom simulated images with those acquired. In terms of average pixel values, the maximum differences were below 15%, while the noise values were in agreement with a maximum difference of 20%. The relative trends of contrast to noise ratio versus beam energy and intensity were well simulated. Total calculation times were below 3 seconds for clinical images with pixel size of actual dimensions less than 0.2 mm. The application proved to be efficient and realistic. Short calculation times and the accuracy of the results obtained make this software a useful tool for training operators and dose optimisation studies

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