No description available.

No description available.

The primary objective of this research was to investigate the feasibility of calibrating ultrasound scanners to measure surface impedance from reflection data. The method proposed uses calibration curves from known impedance interfaces. This plot, or calibration curve, may then be used, with interpolation, to relate measured grey level to impedance for the characterization of tissue specimens with unknown properties. This approach can be used independent of different medical ultrasound scanner systems to solve for reproducible tissue impedance values without offline data processing and complicated custom electronics. <p>Two medical ultrasound machines from different manufacturers were used in the experiment; a 30 MHz and a 7.5 MHz machine. The calibration curves for each machine were produced by imaging the interfaces of a vegetable oil floating over varying salt solutions. <p>To test the method, porcine liver, kidney, and spleen acoustical impedances were determined by relating measured grey levels to reflection coefficients using calibration curves and then inverting the reflection coefficients to obtain impedance values. The 30 MHz ultrasound machines calculated tissue impedances for liver, kidney, and spleen were 1.476 ± 0.020, 1.486 ± 0.020, 1.471 ± 0.020 MRayles respectively. The 7.5 MHz machines tissue impedances were 1.467 ± 0.088, 1.507 ± 0.088, and 1.457 ± 0.088 MRayles respectively for liver, kidney and spleen. The differences between the two machines are 0.61%, 1.41%, and 0.95% for the impedance of liver, kidney, and spleen tissue, respectively. If the grey level is solely used to characterize the tissue, then the differences are 45.9%, 40.3%, and 39.1% for liver, kidney, and spleen between the two machines. The results support the hypothesis that tissue impedance can be determined using calibration curves and be consistent between multiple machines.

The primary objective of this research was to investigate the feasibility of calibrating ultrasound scanners to measure surface impedance from reflection data. The method proposed uses calibration curves from known impedance interfaces. This plot, or calibration curve, may then be used, with interpolation, to relate measured grey level to impedance for the characterization of tissue specimens with unknown properties. This approach can be used independent of different medical ultrasound scanner systems to solve for reproducible tissue impedance values without offline data processing and complicated custom electronics. <p>Two medical ultrasound machines from different manufacturers were used in the experiment; a 30 MHz and a 7.5 MHz machine. The calibration curves for each machine were produced by imaging the interfaces of a vegetable oil floating over varying salt solutions. <p>To test the method, porcine liver, kidney, and spleen acoustical impedances were determined by relating measured grey levels to reflection coefficients using calibration curves and then inverting the reflection coefficients to obtain impedance values. The 30 MHz ultrasound machines calculated tissue impedances for liver, kidney, and spleen were 1.476 ± 0.020, 1.486 ± 0.020, 1.471 ± 0.020 MRayles respectively. The 7.5 MHz machines tissue impedances were 1.467 ± 0.088, 1.507 ± 0.088, and 1.457 ± 0.088 MRayles respectively for liver, kidney and spleen. The differences between the two machines are 0.61%, 1.41%, and 0.95% for the impedance of liver, kidney, and spleen tissue, respectively. If the grey level is solely used to characterize the tissue, then the differences are 45.9%, 40.3%, and 39.1% for liver, kidney, and spleen between the two machines. The results support the hypothesis that tissue impedance can be determined using calibration curves and be consistent between multiple machines.

Ultrasound-based tissue characterization has been an active eld of cancer detection in the past decades. The main concept behind various techniques is that the returning ultrasound echoes carry tissue-dependent information that can be used to distinguish tissue types. Recently, a new paradigm for tissue typing has been proposed which uses ultrasound Radio Frequency (RF) echoes, recorded continuously from a xed location of the tissue, to extract tissue-dependent information. This is hereafter referred to as RF time series. The source of tissue typing information in RF time series is not a well known concept in the literature. However, there are two main hypotheses that describe the informativeness of variations in RF time series. Such information could be partly due to heat induction as a result of consistent eradiation of tissue with ultrasound beams which results in a virtual displacement in RF echoes, and partly due to the acoustic radiation force of ultrasound beams resulting in micro-vibration inside tissue. In this thesis, we further investigate RF time series signals, collected at high frequencies, by analyzing the properties of the RF displacements. It will be shown that such displacements exhibit oscillatory behavior, emphasizing on the possible micro-vibrations inside tissue, as well as linear incremental trend, indicating the e ect of heat absorbtion of tissue. i The main focus of this thesis is to study the oscillatory behavior of RF displace- ments in order to extract tissue-dependent features based on which tissue classi ca- tion is performed. Using various linear and nonlinear tools, we study the properties of such displacements in both frequency and time domain. Nonlinear analysis, based on the theory of dynamical systems, is used to study the dynamical and geometrical properties of RF displacements in the time domain. Using Support Vector Machine (SVM), di erent tissue typing experiments are performed to investigate the capability of the proposed features in tissue classi ca- tion. It will be shown that the combination of such features can distinguish between di erent tissue types almost perfectly. In addition, a feature reduction algorithm, based on principle component analysis (PCA), is performed to reduce the number of features required for a successful tissue classi cation. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2012-03-29 13:52:10.874

About 50% of cancer patients are treated with X-ray radiation therapy; however, with current treatment feedback, the effects and the efficacy of the treatment are generally detected several weeks/months after treatment completion. This makes the adjustment of the treatment based on early response, and identification of non-responding patients, nearly impossible. In this thesis a novel method combining optical coherence tomography and a gamut of image analysis methods is explored as a potential approach to detecting tissue variability. Applying texture analysis to the optical coherence tomography images may allow for the tracking of radiation therapy induced cell microstructural changes in cancer patients and help in the adjustment of treatment based on early response.

About 50% of cancer patients are treated with X-ray radiation therapy; however, with current treatment feedback, the effects and the efficacy of the treatment are generally detected several weeks/months after treatment completion. This makes the adjustment of the treatment based on early response, and identification of non-responding patients, nearly impossible. In this thesis a novel method combining optical coherence tomography and a gamut of image analysis methods is explored as a potential approach to detecting tissue variability. Applying texture analysis to the optical coherence tomography images may allow for the tracking of radiation therapy induced cell microstructural changes in cancer patients and help in the adjustment of treatment based on early response.

O conhecimento do coeficiente de atenuação linear (µ) é de extrema importância para estudos de contraste em imagens de radiodiagnóstico, dose e caracterização de materiais. Parâmetros como a densidade eletrônica (?e), o número atômico médio (Z¯), entre outros, podem ser determinados a partir do coeficiente de atenuação linear em diferentes energias. A proposta deste trabalho é determinar experimentalmente coeficientes de atenuação linear de 80 amostras de tecidos mamários (classificadas previamente como tecido adiposo, tecido glandular, fibroadenoma ou carcinomas) e, posteriormente, extrair parâmetros que possibilitem a caracterização e diferenciação desses tecidos. Os coeficientes de atenuação linear foram medidos usando geometria de feixe estreito, no intervalo de energia entre 10 e 50 keV, utilizando um tubo de raios X com anodo de tungstênio (W) e um detector dispersivo em energia de Si (SDD). Dois modelos de parametrizações foram utilizadas para extrair ?e e Z¯. As metodologias de determinação de µ e de parametrização foram validadas utilizando 8 materiais equivalentes a tecido (4 soluções e 4 sólidos). Os resultados obtidos para tecidos mamários foram comparados com predições teóricas, obtidas usando a regra das misturas, e com dados experimentais previamente publicados, apresentando diferenças máximas de até 7%. Foram também estudadas as variações de µ intra- e inter-amostras de um mesmo grupo, obtendo variações máximas de 5% e 12%, respectivamente. Foi mostrado que o coeficiente de atenuação linear consegue distinguir apenas o tecido adiposo dos demais grupos de tecidos para energia menores de 24 keV. Finalmente, foi elaborado um modelo de diagnóstico, baseados nos parâmetros ?e e Z¯. As análises estatísticas mostram que 71% das amostras foram classificadas corretamente. / The knowledge of the linear attenuation coefficient (µ) is of extreme importance for radiodiagnostic image contrast studies, dose and material characterization. Parameters as electronic density (?e), average atomic number (Z¯), among others, can be determined using the linear attenuation coefficient at different energies. The purpose of this work is to experimentally determine the linear attenuation coefficient of 80 mammary tissues samples (classified as adipose tissue, glandular tissue, fibroadenoma or carcinoma) and then extract parameters that allow the characterization and differentiation of those tissues. The linear attenuation coefficients were measured using narrow bean geometry, with an energy interval between 10 and 50 keV, using a x-ray tube with a tungsten (W) anode and a Silicon energy dispersive detector (SDD). Two parameterization models were used to extract ?e and Z¯. The methodologies of determination of µ and parameterizations were validated using 8 tissue equivalent materials (4 solutions and 4 solids). The results obtained for mammary tissues were compared with theoretical predictions, using the mixture rule, and with previously published experimental data, presenting maximum differences of 7%. Intra and between samples variations of the same group were also studied, obtaining maximum variations of 5% and 12%, respectively. The linear attenuation coefficient was able to differentiate only the adipose tissue from others tissues groups, for energies below 24 keV. At last, a diagnostic model was elaborated, based on ?e and Z¯ parameters. The statistical analysis showed that 71% of the samples were correctly classified.

No description available.

The goal of this research is to achieve accurate characterization of multi-layered soft tissues in three dimensions using focused ultrasound. The characterization of the acoustic parameters of each tissue layer is formulated as recursive processes of forward- and inverse- scattering. Forward scattering deals with the modeling of focused ultrasound wave propagation in multi-layered tissues, and the computation of the focused wave amplitudes in the tissues based on the acoustic parameters of the tissue as generated by inverse scattering. The model for mapping the tissue acoustic parameters to focused waves is highly nonlinear and stochastic. In addition, solving (or inverting) the model to obtain tissue acoustic parameters is an ill-posed problem. Therefore, a nonlinear stochastic inverse scattering method is proposed such that no linearization and mathematical inversion of the model are required. Inverse scattering aims to estimate the tissue acoustic parameters based on the forward scattering model and ultrasound measurements of the tissues. A multi-scale stochastic filter (MSF) is proposed to perform inverse scattering. MSF generates a set of tissue acoustic parameters, which are then mapped into focused wave amplitudes in the multi-layered tissues by forward scattering. The tissue acoustic parameters are weighted by comparing their focused wave amplitudes to the actual ultrasound measurements. The weighted parameters are used to estimate a weighted Gaussian mixture as the posterior probability density function (PDF) of the parameters. This PDF is optimized to achieve minimum estimation error variance in the sense of the posterior Cramer-Rao bound. The optimized posterior PDF is used to produce minimum mean-square-error estimates of the tissue acoustic parameters. As a result, both the estimation error and uncertainty of the parameters are minimized. PDF optimization is formulated based on a novel multi-scale PDF analysis framework. This framework is founded based on exploiting the analogy between PDFs and analog (or digital) signals. PDFs and signals are similar in the sense that they represent characteristics of variables in their respective domains, except that there are constraints imposed on PDFs. Therefore, it is reasonable to consider a PDF as a signal that is subject to amplitude constraints, and as such apply signal processing techniques to analyze the PDF. The multi-scale PDF analysis framework is proposed to recursively decompose an arbitrary PDF from its fine to coarse scales. The recursive decompositions are designed so as to ensure that requirements such as PDF constraints, zero-phase shift and non-creation of artifacts are satisfied. The relationship between the PDFs at consecutive scales is derived in order for the PDF optimization process to recursively reconstruct the posterior PDF from its coarse to fine scales. At each scale, PDF reconstruction aims to reduce the variances of the posterior PDF Gaussian components, and as a result the confidence in the estimate is increased. The overall posterior PDF variance reduction is guided by the posterior Cramer-Rao bound. A series of experiments is conducted to investigate the performance of the proposed method on ultrasound multi-layered soft tissue characterization. Multi-layered tissue phantoms that emulate ocular components of the eye are fabricated as test subjects. Experimental results confirm that the proposed MSF inverse scattering approach is well suited for three-dimensional ultrasound tissue characterization. In addition, performance comparisons between MSF and a state-of-the-art nonlinear stochastic filter are conducted. Results show that MSF is more accurate and less computational intensive than the state-of-the-art filter.