Blind deconvolution of medical ultrasound images: parametric inverse filtering approach

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.910179The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a ldquohybridizationrdquo of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the ldquohybridrdquo approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolution algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used

Deconvolution, differentiation and Fourier transformation algorithms for noise-containing data based on splines and global approximation

One of the main problems in the analysis of measured spectra is how to reduce the influence of noise in data processing. We show a deconvolution, a differentiation and a Fourier Transform algorithm that can be run on a small computer (64 K RAM) and suffer less from noise than commonly used routines. This objective is achieved by implementing spline based functions in mathematical operations to obtain global approximation properties in our routines. The convenient behaviour and the pleasant mathematical character of splines makes it possible to perform these mathematical operations on large data input in a limited computing time on a small computer system. Comparison is made with widely used routines

Investigation of Statistical and Imaging Methods for Luminescence Detection of Irradiated Ingredients

This project investigated two potential approaches to improving the reliability of lumines-cence methods for detecting minor irradiated ingredients in foods. Whereas in the 1980’s there were no validated methods for laboratory detection of irradiated foods, work conducted in the UK and elsewhere by the mid 1990’s had resulted in the development of a series of physical, chemical and biological methods capable of detecting a range of irradiated food classes. Of these the luminescence methods embodied in EN1788 (Thermoluminescence) and EN13751 (Photostimulated luminescence) standards have been applied to detection of a vari-ety of products including herbs and spices, and seafood. In common with the other EN stan-dard methods almost all validation work had been originally conducted using pure irradiated or unirradiated ingredients. Yet application experience had shown the presence of mixed products containing both irradiated and unirradiated ingredients. A short study was commis-sioned by MAFF to investigate the impact of blending on standard EN1788 methods, and on the provisional draft EN13751 (the standard having been published in the meantime) method. This showed the impact of dilution of irradiated material between 10% and 0.1% concentra-tions on detection rates, which unsurprisingly are reduced by extreme dilution. UK labelling regulation, both before and after adoption of the European Directive on Food Irradiation, call for labelling of all irradiated ingredients regardless of concentration or origin within the final product. This study was therefore motivated by the recognition of the long term need for im-proved methods to improve reliability at low concentrations. Two complementary approaches were investigated. The project first examined whether TL data collected using the EN1788 method could be enhanced using advanced statistical proce-dures. Data sets from the SURRC TL archive, and from project CSA4790 were used both to define the characteristics of irradiated and unirradiated end members, and to assess classifica-tion methods using the controlled blending experimental data sets of CSA 4790. Multivariate analyses, based on principal components analysis and discriminant analysis of glow curve data; kinetic deconvolution approaches coupled to PCA and DA, and neural analyses were investigated and compared with detection rates achieved using expert visual classification. To complement this experiments were undertaken to explore the potential of using focussed laser stimulation to produce spatially resolved measurements from mineral grains separated from foods. Two systems were evaluated based on IR and visible band lasers. Work was under-taken to explore sample presentation and to assess the ability of this approach to distinguish mixtures of irradiated and unirradiated grains. The statistical work was successful in developing three approaches which could be used for objective identification of irradiated materials. Pure irradiated and unirradiated data sets from 150 sample pairs were obtained having searched the SUERC archive of more than 3500 lu-minescence analyses. These were used to set up multivariate analyses based on the ap-proaches outlined above. Performance in recognising irradiated ingredients using these meth-ods was then assessed with data drawn from the MAFF blending investigation, comprising 160 permutations of irradiated and unirradiated herbs and spices at 10%, 1% and 0.1% con-centrations. It was possible to achieve good detection rates with alatistical approaches, the best approaches inigated being the use of glow curve deconvolution coupwith li discrimination, and the use of neural appros. The absolute performance achieved matched that opert visual clfication utilising the revised EN1788 criterwhich were adopted within the international standauring course of this project. The use of ad-vancedtistical methods, while not adding performance, can pde objective support to visual classifications. During performance assessment it was aloted that theformance of all methods wasficiently close to infer that detections rates are most dependent on the statistical presence or absence of irradiated grains within the extracted samples used for TL analysis. This raises practical suggestions for improving detection rates at low concentrations based on the use of larger samples and more specific mineral separation approaches. These may be worth investigating further. Laser scanning approaches were also investigated using highly focussed laser beams to stimulated luminescence sequentially from different parts of separated mineral samples. Work was conducted using a system which had been developed in earlier work at SUERC, and then followed by additional investigation using an improved instrument built during the project. Initial work confirmed the feasibility of using laser scanning approaches to obtain spatially resolved luminescence data at or near the dimensions of individual mineral grains. Practical obstacles included the recognition that laser scattering from surfaces coated with mineral grains introduced an element of cross-talk between different parts of the sample, and difficulties in accurate re-positioning of the sample using the first generation prototype in-strument. Work was conducted to investigate a series of different sample presentation media to improve the former, and to incorporate high precision mechanical and optoelectronic means of re-positioning samples between initial measurements, external irradiation, and sub-sequent re-measurement. Both IR and visible band semiconductor lasers were investigated with successful production of single grain images. The short and medium term reliability of the lasers used was acceptable. The lasers used both however eventually failed, which sug-gests that long term lifetime may be an issue for further work. Of the two lasers the IR laser in particular gave a good signal to background ratio for discriminating between irradiated and unirradiated grains. Quantitative analysis of the grain resolved images confirms the potential of this approach in identifying minor irradiated components. The overall conclusions of the work are that both statistical approaches and imaging instru-ments are able to enhance current methods. The observation that visual classification can match the performance even of deconvolution or neural approaches suggests that future effort should be directed more towards improvement of grain statistics in conventional measure-ments, and in further development and investigation of imaging approaches. In these ways it can anticipated that the performance of standard luminescence methods for detecting dilute mixtures of irradiated and unirradiated food ingredients could be significantly improved. To do so would further enhance work conducted by FSA and other bodies to ensure that regula-tions governing the use of irradiation in food processing and the labelling of imported foods are followed