Direct visualisation of B-1 inhomogeneity by flip angle dependency

Inhomogeneities in the spatial distribution of the excitatory Radio Frequency (RF) field, are still a dominant source of artifacts and loss of signal to noise ratio in MR imaging experiments, A number of strategies have been proposed to quantify this distribution, However, in this technical note we present a relatively simple MR imaging procedure which can be used to visualise RF inhomogeneities directly either by means of the magnitude or the phase of an image. To visualise the RF field distribution in both the inner and outer volumes of the coil, we have performed experiments in which the entire coil is submerged in a non-conducting fluid, To the best of our knowledge this strategy has not been used previously in order to evaluate coil performance, Finally, we demonstrate that the method is sensitive enough to reveal the effects of the sample properties on the effective RF wavelength of the transmitted field. (C) 1997 Elsevier Science Inc.

The effect of high-pass filtering on TEOAE in 2-month-old infants

The aim of the present study was to investigate the effect of high-pass filtering on TEOAE obtained from 2-month-old infants as a function of filter cut-off frequency, activity states and pass/fail status of infants. Two experiments were performed. In Experiment 1, 100 2-month-old infants (200 ears) in five activity states (asleep, awake but peaceful, sucking a pacifier, feeding, restless) were tested by use of TEOAE technology. Five different filter conditions were applied to the TEOAE responses post hoc. The filter conditions were set at 781 Hz (default setting), 1.0, 1.2, 1.4 and 1.6 kHz. Results from this experiment showed that TEOAE parameters, such as whole-wave reproducibility (WR) and signal-to-noise ratio (SNR) at 0.8 kHz and 1.6 kHz, changed as a function of the cut-off frequency. The findings suggest that the 1.6 kHz and 1.2 kHz filter conditions are optimal for WR and SNR pass/fail criteria, respectively. Although all infant recordings appeared to benefit from the filtering, infants in the noisy states seemed to benefit the most. In Experiment 2, the high-pass filtering technique was applied to 23 infants (35 ears) who apparently failed the TEOAE tests on initial screening but were subsequently awarded a pass status based on the results from a follow-up auditory brainstem response (ABR) assessment. The findings showed a significant decrease in noise contamination of the TEOAE with a corresponding significant increase in WR. With high-pass filtering at 1.6 kHz, 21/35 ears could be reclassified into the pass category.

A normative study of distortion product otoacoustic emissions in six-year-old school children

Evoked otoacoustic emissions have demonstrated potential for application in the community-based hearing screening of paediatric populations. Distortion-product otoacoustic emissions (DPOAEs), as opposed to transient evoked otoacoustic emissions (TEOAEs), have not been extensively researched in this regard. The current study aimed to describe the range of DPOAE values obtained in a large cohort (1576 ears) of 6-year-old children in school settings and to examine possible ear asymmetry, gender and history of ear infection effects on the data. Results indicated a variety of significant effects, particularly in the high frequencies, for DPOAE signal-to-noise ratio. The measurement parameter, DPOAE amplitude (DP-amp), was found to display potentially less clinical applicability due to large standard deviation values. Use of descriptive normative data, as derived in the present investigation, may contribute toward future improvements in the hearing screening of 6-year-old schoolchildren

Design of microchannel free-space optical interconnects based on vertical-cavity surface-emitting laser arrays

We investigate the design of free-space optical interconnects (FSOIs) based on arrays of vertical-cavity surface-emitting lasers (VCSELs), microlenses, and photodetectors. We explain the effect of the modal structure of a multimodeVCSEL beam on the performance of a FSOI with microchannel architecture. A Gaussian-beam diffraction model is used in combination with the experimentally obtained spectrally resolved VCSEL beam profiles to determine the optical channel crosstalk and the signal-to-noise ratio (SNR) in the system. The dependence of the SNR on the feature parameters of a FSOI is investigated. We found that the presence of higher-order modes reduces the SNR and the maximum feasible interconnect distance. We also found that the positioning of a VCSEL array relative to the transmitter microlens has a significant impact on the SNR and the maximum feasible interconnect distance. Our analysis shows that the departure from the traditional confocal system yields several advantages including the extended interconnect distance and/or improved SNR. The results show that FSOIs based on multimode VCSELs can be efficiently utilized in both chip-level and board-level interconnects. (C) 2002 Optical Society of America.

Optimizing electrode positioning in 3-lead ECG chest devices

Although the 12-lead electrocardiogram has become an essential medical and research tool, many current and envisaged applications would benefit from simpler devices, using 3-lead ECG configuration. This is particularly true for Ambient Assisted Living (in a broad perspective). However, the chest anatomy of female patients, namely during pregnancy, can hamper the adequate placement of a 3-lead ECG device and, very often, electrodes are placed below the chest rather than at the precise thoracic landmarks. Thus, the aim of this study was to compare the effect of electrode positioning on the ECG signal of pregnant women and provide guidelines for device development. The effect of breast tissue on the ECG signal was investigated by relating breast size with the signal-to-noise ratio, root mean square and R-wave amplitude. Results show that the 3-lead ECG should be placed on the breast rather than under the breast and indicate positive correlation between breast size and signal-to-noise ratio.

DWI: factores que influenciam a medição do coeficiente de difusão aparente (ADC) no estudo da mama

Mestrado em Radiações Aplicadas às Tecnologias da Saúde. Área de especialização: Ressonância Magnética

A hybrid MLS technique for room impulse response estimation

The measurement of room impulse response (RIR) when there are high background noise levels frequently means one must deal with very low signal-to-noise ratios (SNR). if such is the case, the measurement might yield unreliable results, even when synchronous averaging techniques are used. Furthermore, if there are non-linearities in the apparatus or system time variances, the final SNR can be severely degraded. The test signals used in RIR measurement are often disturbed by non-stationary ambient noise components. A novel approach based on the energy analysis of ambient noise - both in the time and in frequency - was considered. A modified maximum length sequence (MLS) measurement technique. referred to herein as the hybrid MLS technique, was developed for use in room acoustics. The technique consists of reducing the noise energy of the captured sequences before applying the averaging technique in order to improve the overall SNRs and frequency response accuracy. Experiments were conducted under real conditions with different types of underlying ambient noises. Results are shown and discussed. Advantages and disadvantages of the hybrid MLS technique over standard MLS technique are evaluated and discussed. Our findings show that the new technique leads to a significant increase in the overall SNR. (C) 2008 Elsevier Ltd. All rights reserved.

Monitorização de pesticidas em águas de Esposende

De forma a proteger o ambiente e a saúde humana, é imperativo evitar, prevenir ou reduzir as concentrações prejudiciais de poluentes nocivos na água subterrânea. A necessidade da obtenção de níveis de protecção da água subterrânea, encontra-se estabelecida em normas de qualidade e devem ser desenvolvidas metodologias que permitam a avaliação do estado químico da água subterrânea. Este trabalho experimental centrou-se no desenvolvimento de uma metodologia analítica de detecção e quantificação por cromatografia gasosa com detector de captura de electrões dos pesticidas atrazina e respectivos metabolitos (desetilatrazina e deisopropilatrazina), simazina, terbutilazina e o metabolito desetiterbutilazina, folpete, dimetoato, diazinão, malatião, cloropirifos e o azinfos-metilo em águas de poços. O estudo progressivo baseou-se na colheita de água a 20 poços agrícolas na zona de Esposende, área considerada pelo Ministério da Agricultura do Desenvolvimento Rural e Pescas como sendo uma zona vulnerável. O método utilizado para a validação da técnica cromatográfica baseou-se na norma ISO 8466-1:1990. Os parâmetros de validação considerados foram: especificidade/selectividade, capacidade de identificação, limites de detecção e quantificação, relação sinal/ruído, linearidade e curva de calibração, precisão (repetibilidade, precisão intermédia e reprodutibilidade), eficiência de extracção e arrastamento. O método demonstrou ser capaz de identificar e quantificar os analitos, sem interferência de outros compostos. Obteve-se um valor para os parâmetros da precisão inferior a 10%, enquanto os mais baixos limites de detecção e de quantificação foram, respectivamente, 0,014 e 0,047 μg L-1. Na preparação de amostras optou-se pelo método de extracção em fase sólida, tendo sido testadas cinco diferentes tipos de colunas extractivas; Lichrolut® EN/RP-18; Strata SDB-L e C18-E; Chromabond HR-P e HR-X, sendo que as colunas Lichrolut® EN/RP- 18 apresentaram melhores resultados para a globalidade dos pesticidas. Da análise efectuada aos 20 poços agrícolas verificou-se que apenas 3 não apresentavam qualquer vestígio dos pesticidas monitorizados, sendo que as restantes apresentavam valores entre 0,05 e 53,2 μg L-1, valores superiores aos impostos pela legislação em vigor (Decreto-Lei n.º 208/2008 de 28 de Outubro para água subterrânea e Decreto-Lei nº306/2007 referente a água para consumo). Verificou-se que os proprietários dos poços agrícolas, dos quais se procedeu à amostragem de água para análise não têm a consciência da falta de qualidade dessa água, nem dos malefícios que possam advir do seu consumo.

Does independent component analysis play a role in unmixing hyperspectral data?

Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2)sources are statistically independent. Independent factor analysis (IFA) extends ICA to linear mixtures of independent sources immersed in noise. Concerning hyperspectral data, the first assumption is valid whenever the multiple scattering among the distinct constituent substances (endmembers) is negligible, and the surface is partitioned according to the fractional abundances. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be statistically independent, this compromising the performance of ICA/IFA algorithms in hyperspectral unmixing. This paper studies the impact of hyperspectral source statistical dependence on ICA and IFA performances. We conclude that the accuracy of these methods tends to improve with the increase of the signature variability, of the number of endmembers, and of the signal-to-noise ratio. In any case, there are always endmembers incorrectly unmixed. We arrive to this conclusion by minimizing the mutual information of simulated and real hyperspectral mixtures. The computation of mutual information is based on fitting mixtures of Gaussians to the observed data. A method to sort ICA and IFA estimates in terms of the likelihood of being correctly unmixed is proposed.

Guest editorial: OPTIMAX 2013

In 2012 we were awarded an Erasmus Intensive Programme grant to facilitate OPTIMAX 2013, a three week duration residential summer school held within the UK during August 2013. The summer school helped to further develop student radiographer skills in optimising x-radiation dose and image quality. With a major emphasis on visual techniques to determine image quality, lesion visibility, lesion detection performance and physical measures of image quality (eg signal to noise ratio (SNR)) we conducted controlled laboratory experiments on phantoms using Computed Radiography, CT and Full Field Digital Mammography. Mathematical modelling was used for radiation dose estimation. Sixty seven people from 5 European countries participated. This included 49 PhD, MSc and BSc students. Discipline areas included radiography, physics, biomedical science and nuclear medicine.

Efeito dos parâmetros técnicos na dose e qualidade de imagem num sistema de radiografia computorizada

Mestrado em Radiações Aplicadas às Tecnologias da Saúde - Ramo de especialização: Imagem Digital com Radiação X

ECG Biometrics: A Template Selection Approach

Electrocardiography (ECG) biometrics is emerging as a viable biometric trait. Recent developments at the sensor level have shown the feasibility of performing signal acquisition at the fingers and hand palms, using one-lead sensor technology and dry electrodes. These new locations lead to ECG signals with lower signal to noise ratio and more prone to noise artifacts; the heart rate variability is another of the major challenges of this biometric trait. In this paper we propose a novel approach to ECG biometrics, with the purpose of reducing the computational complexity and increasing the robustness of the recognition process enabling the fusion of information across sessions. Our approach is based on clustering, grouping individual heartbeats based on their morphology. We study several methods to perform automatic template selection and account for variations observed in a person's biometric data. This approach allows the identification of different template groupings, taking into account the heart rate variability, and the removal of outliers due to noise artifacts. Experimental evaluation on real world data demonstrates the advantages of our approach.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Interictal Spike EEG Source Analysis in Hypothalamic Hamartoma Epilepsy

Objective: The epilepsy associated with the hypothalamic hamartomas constitutes a syndrome with peculiar seizures, usually refractory to medical therapy, mild cognitive delay, behavioural problems and multifocal spike activity in the scalp electroencephalogram (EEG). The cortical origin of spikes has been widely assumed but not specifically demonstrated. Methods: We present results of a source analysis of interictal spikes from 4 patients (age 2–25 years) with epilepsy and hypothalamic hamartoma, using EEG scalp recordings (32 electrodes) and realistic boundary element models constructed from volumetric magnetic resonance imaging (MRIs). Multifocal spike activity was the most common finding, distributed mainly over the frontal and temporal lobes. A spike classification based on scalp topography was done and averaging within each class performed to improve the signal to noise ratio. Single moving dipole models were used, as well as the Rap-MUSIC algorithm. Results: All spikes with good signal to noise ratio were best explained by initial deep sources in the neighbourhood of the hamartoma, with late sources located in the cortex. Not a single patient could have his spike activity explained by a combination of cortical sources. Conclusions: Overall, the results demonstrate a consistent origin of spike activity in the subcortical region in the neighbourhood of the hamartoma, with late spread to cortical areas.

Sensitivity correction of images obtained with the prototype Clear-PEM in pre-clinical environment

Dissertation presented at Faculdade de Ciências e Tecnologia Universidade Nova de Lisboa to obtain a Master Degree in Biomedical Engineering