No description available.

Information obtained from ultrasound images of fetal heads is often used to screen for various types of physical abnormality. In particular, at around 16 to 23 weeks' gestation two-dimensional cross-sections are examined to assess whether a fetus is affected by Neural Tube Defects, a class of disorders that includes Spina Bifida. Unfortunately, ultrasound images are of relatively poor quality and considerable expertise is required to extract meaningful information from them. Developing an ultrasound image recognition method that does not rely upon an experienced sonographer is of interest. In the course of this work we review standard statistical image analysis techniques, and explain why they are not appropriate for the ultrasound image data that we have. A new iterative method for edge detection based on a kernel function is developed and discussed. We then consider ways of improving existing techniques that have been applied to ultrasound Images. Storvik (1994)'s algorithm is based on the minimisation of a certain energy function by simulated annealing. We apply a cascade type blocking method to speed up this minimisation and to improve the performance of the algorithm when the noise level is high. Kass, Witkin and Terzopoulos (1988)'s method is based on an active contour or 'snake' which is deformed in such a way as to minimise a certain energy function. We suggest modifications to this energy function and use simulated annealing plus iterated conditional modes to perform the associated minimisation. We demonstrate the effectiveness of the new edge detection method, and of the improvements to the existing techniques by means of simulation studies.

Electrical impedance tomography (EIT) is a technique in which images representing the cross-sectional distribution of electrical impedance within a threedimensional object are reconstructed from measurements on the object surface. In this work, some developments of image reconstruction algorithms aimed at increasing the value of this technique in the field of medical diagnosis are studied. The electrical properties of biological tissue and the possibilities for medical applications of EIT are first reviewed. The physical and mathematical basis for EIT is then. examined with particular regard for the assumptions required. Following a review of published work on image reconstruction methods, a set of specifications thought useful to advance the utility of EIT as a clinical imaging modality is proposed, together with an approach to image reconstruction designed to fulfil these specifications. A series of computer simulations of the image reconstruction problem is then used to investigate the performance of this reconstruction approach on simple, known, impedance distributions, and to develop the method to the stage of a complete reconstruction algorithm. The algorithm is then tested on a series of data sets produced by measurements on a physical phantom, and on a set of measurements made on a volunteer human subject.

A Constrained Optimisation technique is described for the reconstruction of temporal resistivity images. The approach solves the Inverse problem by optimising a cost function under constraints, in the form of normalised boundary potentials. Mathematical models have been developed for two different data collection methods for the chosen criterion. Both of these models express the reconstructed image in terms of one dimensional (I-D) Lagrange multiplier functions. The reconstruction problem becomes one of estimating these 1-D functions from the normalised boundary potentials. These models are based on a cost criterion of the minimisation of the variance between the reconstructed resistivity distribution and the true resistivity distribution. The methods presented In this research extend the algorithms previously developed for X-ray systems. Computational efficiency is enhanced by exploiting the structure of the associated system matrices. The structure of the system matrices was preserved in the Electrical Impedance Tomography (EIT) implementations by applying a weighting due to non-linear current distribution during the backprojection of the Lagrange multiplier functions. In order to obtain the best possible reconstruction it is important to consider the effects of noise in the boundary data. This is achieved by using a fast algorithm which matches the statistics of the error in the approximate inverse of the associated system matrix with the statistics of the noise error in the boundary data. This yields the optimum solution with the available boundary data. Novel approaches have been developed to produce the Lagrange multiplier functions. Two alternative methods are given for the design of VLSI implementations of hardware accelerators to improve computational efficiencies. These accelerators are designed to implement parallel geometries and are modelled using a verification description language to assess their performance capabilities.

No description available.

Most EIT machine operates at high frequencies above 10 kHz. Biological systems demonstrate dispersions of electrical impedance characteristics at very low frequencies below 2 kHz due to the presence of membrane surrounding the cells and diffusion polarisation effects. A study was made on the feasibility of the use of low frequencies in a range of 1.12 Hz to 4.55 kHz in EIT. One high frequency of 77.712 kHz similar to that normally used in common EIT was also used as a comparison. The impedance measurements employed a four-terminal method using the BULFIS, an ultra low frequency impedance spectrometer and used conducting and insulating material as the objects/phantoms. The results show that the conductance and capacitance of a metal object disperses at frequency range of 0.1 -10 kHz, which is consistent to the electrical properties of a double layer forming at the metal-electrolyte interface similar to the electrical properties of a membrane. The reconstructed images reveal that at low frequencies the conducting and the insulating bodies were indistinguishable. They appear differently at high frequencies above 4.55 kHz indicating that the use of multi frequency instrumentation in EIT covering the very low frequency range provides information that instrumentation restricted to frequencies above 10 kHz does not supply. While the internal structure of the double layers could not be delineated, the presence of the double layers could be readily detected by the behaviour of the images as the frequency was varied. This has potential for EIT because it might allow the detection of structures from the variation of the images with frequency. This variation with frequency does not occur at the higher frequencies more usually used for EIT.

Electrical impedance tomography (EIT) is an imaging technique that aims to reconstruct the internal conductivity distribution of a body, based on the electrical measurements taken on its periphery. While relatively new it has received attention as a possible new medical imaging technique which offers non-hazardous applications and low-cost instrumentation. To understand the full potential of this new mode of imaging, a numerical modelling method has been used in order to investigate the behaviour of an EIT system. Using this mode of analysis, it is possible to simulate many experiments that otherwise physically would be very time consuming and expensive. Such investigation will include the effect and quantification of various physical conditions which have effects on the obtained boundary voltages of an EIT system. An aim of EIT is the ability to reconstruct accurate images of internal conductivity distributions from the measured boundary voltages. Image reconstruction in EIT using the sensitivity algorithm is generally based on the assumption that the initial conductivity distribution of the body being imaged is uniform. The technique of image reconstruction using the sensitivity algorithm is described and reconstructed images are presented. Improvements in image quality and accuracy are demonstrated when accurate a-priori 'anatomical' information, in the form of a model of the distribution of conductivity within the region to be imaged, are used. In practice correct a-priori information is not available, for example, the conductivity values within the various anatomical regions will not be known. An iterative algorithm is presented which allows the conductivity parameters of the a-priori model to be calculated during image reconstruction. Multi-frequency EIT is a modified approach of the single frequency method by which tissue characterisation has been proposed by imaging the internal conductivity of region over a range of frequencies. However, due to instrumentation drawbacks, only the real parts of the boundary voltages are presently measured. These real only voltages have sofar (in practice) been used to reconstruct images of the changes in internal conductivity of a region with frequency. The penalty for ignoring the imaginary parts of the data are presented and results obtained show that to accurately image the internal conductivity of a region, not only complex data are needed, but also some a-priori information about the region may be necessary.

<p> Prostate cancer is among the most prevalent and deadly of malignancies in both the United States and worldwide. Ongoing diagnostic challenges in prostate cancer include differentiating low-risk and high-risk tumors, and monitoring responses to therapy in patients with aggressive disease. Prostate cancer metabolism is characterized by a shift to aerobic glycolysis with lactate production and efflux, as well as increased tricarboxylic acid cycle activity, which has led to the investigation and development of metabolic imaging strategies such as hyperpolarized 13C MRI. However, it is nontrivial to study human prostate cancer metabolism in vivo, and the capability to better characterize tumor metabolism from a variety of disease states would be valuable for metabolic imaging biomarker development. This dissertation focuses on developing ex vivo strategies to measure metabolism in benign and malignant living human prostate tissue. First, because prostate tissue heterogeneity can impact metabolic measurements, we present the engineering of a 600 MHz radiofrequency (RF) microcoil to assess the heterogeneity of freshly acquired human prostate biopsies using microscale diffusion-weighted imaging (DWI). Next, we demonstrate the capability of micro-DWI to determine the biopsy percentage of glandular tissue, setting the stage for establishing the percentage and grade of cancer using this approach. After this, we develop a protocol for nuclear magnetic resonance (NMR) quantification of lactate production and efflux and glutamate fractional enrichment in freshly acquired living human prostate biopsies cultured with [1,6-13C2]glucose. In this study we demonstrate a significantly higher lactate efflux rate coming from low-grade prostate cancer versus benign biopsies in an early-stage patient population. This sets the stage for studies of metabolic fluxes and steady-state metabolite levels in biopsies from patients with aggressive disease before and after non-surgical therapy. Finally, due to recent interest in the potential role of Myc amplification and glutaminolysis upregulation in treatment insensitive castrate-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC), we present metabolic labeling results from a study of primary human prostate tissue slice cultures (TSCs) obtained at surgery and cultured with either [1,6-13C2]glucose or [3-13C]glutamine. Our results are consistent with prior thinking on the role of glucose and glutamine metabolism in treatment-na&iuml;ve prostate cancer.</p><p>

Quantitative Magnetic Resonance has become more and more accepted for clinical trial in many fields. This technique not only can generate qMRI maps (such as T1/T2/PD) but also can be used for further postprocessing including segmentation of brain and characterization of different brain tissue. Another main application of qMRI is to measure the volume of the brain tissue such as the deep Grey Matter (dGM). The deep grey matter serves as the brain's "relay station" which receives and sends inputs between the cortical brain regions. An abnormal volume of the dGM is associated with certain diseases such as Fetal Alcohol Spectrum Disorders (FASD). The goal of this study is to investigate the effect of age on the volume change of the dGM using qMRI. Thirteen patients (mean age= 26.7 years old and age range from 0.5 to 72.5 years old) underwent imaging at a 1.5T MR scanner. Axial images of the entire brain were acquired with the mixed Turbo Spin-echo (mixed -TSE) pulse sequence. The acquired mixed-TSE images were transferred in DICOM format image for further analysis using the MathCAD 2001i software (Mathsoft, Cambridge, MA). Quantitative T1 and T2-weighted MR images were generated. The image data sets were further segmented using the dual-space clustering segmentation. Then volume of the dGM matter was calculated using a pixel counting algorithm and the spectrum of the T1/T2/PD distribution were also generated. Afterwards, the dGM volume of each patient was calculated and plotted on scatter plot. The mean volume of the dGM, standard deviation, and range were also calculated. The result shows that volume of the dGM is 47.5 ±5.3ml (N=13) which is consistent with former studies. The polynomial tendency line generated based on scatter plot shows that the volume of the dGM gradually increases with age at early age and reaches the maximum volume around the age of 20, and then it starts to decrease gradually in adulthood and drops much faster in elderly age. This result may help scientists to understand more about the aging of the brain and it can also be used to compare with the results from former studies using different techniques.

The work in this thesis falls into three sections: (i). the development and application of a computer simulation of the MRI experiment based on the Bloch Equations incorporating all flow and motion effects that would be expected in the body, (ii). a theoretical analysis of factors affecting the efficacy of perfusion and diffusion imaging techniques and (iii). the proposal and evaluation of a new technique for the MRI measurement of perfusion. The simulation provided a powerful analytical tool used in the theoretical work of this thesis. The modularity of the design will enable simple development for future applications. The purpose of the theoretical analysis was to resolve many of the controversial issues arising from the various diffusion and perfusion imaging techniques including: the applicability of the various techniques in different in-vivo systems, the effects of motion artifacts, noise and eddy currents. Some conclusions of great significance were arrived at specifically the importance chosing a flow measurement technique appropriate to the tissue and flow type and the severe effects of motion artifacts in IVIM and phase display imaging. From this analysis a new perfusion imaging technique was derived which was implemented and evaluated in a perfusion phantom and in the calf muscle. Very good results were achieved in the phantom studies, and the results from the calf muscle were promising. On a clinical MRI system the technique could prove very useful.