Avaliação da dose glandular média (MGD) em três sistemas mamográficos

Este estudo tem por objectivos determinar a Dose Glandular Média - Mean Glandular Dose (MGD) - em 3 sistemas de Mamografia e comparar os valores obtidos com os referenciais internacionais. O estudo foi realizado num sistema analógico de Écran-Película (EP) e em dois sistemas de imagem digital (CR e DR). Foi efectuado o cálculo da Entrance Surface Air Kerma (ESAK) e da MGD em três equipamentos a partir de uma amostra de dados referentes a 30 mulheres assintomáticas, com idades compreendidas entre os 40 e 64 anos. Em cada equipamento objecto de análise, foram recolhidos os dados referentes a 10 mulheres. Foram consideradas as projecções crânio-caudal (CC) e oblíqua médio-lateral (MLO). A análise de resultados revelou que o valor de MGD varia quando se compara os três sistemas. Nas incidências CC os valores de MGD obtidos foram de 1,54 mGy (EP), 1,78 mGy (CR) e 0,82 mGy (DR). Nas incidências MLO o valor de MGD foi de 1,53 mGy no sistema EP, de 1,78 mGy no CR e 0,87 mGy no sistema DR. Constata-se que o valor de MGD na incidência de CC é inferior ao valor de MGD na incidência MLO, excepto para o sistema EP. Verifica-se também que o sistema EP apresenta maior variabilidade nos dados de MGD comparativamente com os restantes sistemas. O sistema DR é o que apresenta a menor variabilidade de valores MGD e também valores de MGD mais baixos. Comparando os resultados deste estudo com as referências internacionais, verifica-se que a MGD se encontra abaixo do limite de 2 mGy recomendado. ABSTRACT - This study aims to estimate the Mean Glandular Dose (MGD) associated with three different mammographic systems and compare the results with recommended international reference values. The systems included in the study included a conventional Screen-Film (SF) system and two digital mammography systems (CR and DR). Entrance Surface Air Kerma (ESAK) and MGD associated with each equipment were calculated. A sample of 30 healthy women (age ranging from 40 to 64 years old) undertaking screening mammography was considered in this study. The mammographic exam includes two projections, cranio-caudal (CC) and medio-lateral oblique (MLO). The MGD results obtained for CC projection were 1,54 mGy (SF), 1,78 mGy (CR) and 0,82 mGy (DR). MGD values for the MLO projection were 1,53 mGy (SF), 1,78 mGy (CR) and 0,87 mGy (DR). Results show that MGD value is slightly lower in the CC projection than in MLO, except for the SF system (1,54 mGy; 1,53 mGy). In addition the MGD for the SF system varied more than that associated with the digital systems. The DR system allows a narrow variation of MGD values and also lower MGD values. Comparing this study results with the international references we concluded that MGD values are below the 2 mGy recommended value for the three systems evaluated.

Colloidal interactions in two-dimensional nematics

The interaction between two disks immersed in a 2D nernatic is investigated i) analytically using the tenser order parameter formalism for the nematic configuration around isolated disks and ii) numerically using finite-element methods with adaptive meshing to minimize the corresponding Landau-de Gennes free energy. For strong homeotropic anchoring, each disk generates a pair of defects with one-half topological charge responsible for the 2D quadrupolar interaction between the disks at large distances. At short distance, the position of the defects may change, leading to unexpected complex interactions with the quadrupolar repulsive interactions becoming attractive. This short-range attraction in all directions is still anisotropic. As the distance between the disks decreases, their preferred relative orientation with respect to the far-field nernatic director changes from oblique to perpendicular.

Mammography equipment design: impact on radiographers’ practice

Objectives - Identify radiographers’ postures during frequent mammography procedures related to the mammography equipment and patient characteristics. Methods - A postural task analysis was performed using images acquired during the simulation of mammography positioning procedures. Simulations included craniocaudal/(CC) and mediolateral-oblique/(MLO) positioning in three different settings: radiographers and patients with similar statures, radiographers smaller than the patients and radiographers taller than the patients. Measurements of postural angles were performed by two raters using adequate software and classified according to the European Standard EN1005-4:2005 + A1:2008. Results - The simulations revealed that the most awkward posture in mammography is during the positioning of MLO projection in short-stature patients. Postures identified as causing work-related musculoskeletal disorder (WRMSD) risk were neck extension, arms elevated and the back stooped, presenting angles of 87.2, 118.6 and 63.6, respectively. If radiographers were taller than patients, then the trunk and arm postures were not acceptable. Conclusions - Working in a mammography room leads to awkward postures that can have an impact on radiographers’ health, namely WRMSDs. The results in this study showed that there are non-acceptable postures associated with frequent working procedures in mammography. MLO is the most demanding procedure for radiographer postures and may be related to WRMSDs. Mammography devices should be redesigned considering adjustability for radiographers. Main Messages: • Mammography constraints for radiographers in mammography procedures have not been well studied. • Performing mammography leads to awkward postures that can impact radiographers’ health. • MLO, the most demanding procedure for radiographers, is possibly related to WRMSDs.

Comparação de dois protocolos de reabilitação após reconstrução cirúrgica do ligamento cruzado anterior

Mestrado em Fisioterapia

Radiographers practice concerning breast compression in mammography: gradual versus no gradual

Purpose: Evaluate the type of breast compression (gradual or no gradual) that provides less discomfort to the patient. Methods and Materials: The standard projections were simulated [craniocaudal/(CC) and mediolateral-oblique/(MLO)] with the two breast compressions in 90 volunteers women aged between 19 and 86. The women were organised in groups according to the breast density. The intensity of discomfort was evaluated using the scale that have represented several faces (0-10) proposed by Wong Baker in the end of each simulation. It was also applied an interview using focus group to debate the score that were attributed during pain evaluation and to identify the criteria that were considered to do the classification. Results: The women aged between 19-29y (with higher breast density) classified the pain during no gradual compression as 4 and the gradual compression as 2 for both projections. The MLO projection was considered the most uncomfortable. During the focus group interview applied to this group was highlighted that compression did not promoted pain but discomfort. They considered that the high expectations of pain did not correspond to the discomfort that they felt. Similar results were identified for the older women (30-50y; > 50y). Conclusion: The radiographers should considerer the technique for breast compression. The gradual compression was considered for the majority of the women as the most comfortable regardless of breast density. The MLO projection was considered as uncomfortable due to the positioning (axila and inclusion of pectoral muscle) and due to the higher breast compression compared to the CC projection.

Magma flow pattern in dykes of the Azores revealed by ansiotropy of magnetic susceptibility

The localization of magma melting areas at the lithosphere bottom in extensional volcanic domains is poorly understood. Large polygenetic volcanoes of long duration and their associated magma chambers suggest that melting at depth may be focused at specific points within the mantle. To validate the hypothesis that the magma feeding a mafic crust, comes from permanent localized crustal reservoirs, it is necessary to map the fossilized magma flow within the crustal planar intrusions. Using the AMS, we obtain magmatic flow vectors from 34 alkaline basaltic dykes from São Jorge, São Miguel and Santa Maria islands in the Azores Archipelago, a hot-spot related triple junction. The dykes contain titanomagnetite showing a wide spectrum of solid solution ranging from Ti-rich to Ti-poor compositions with vestiges of maghemitization. Most of the dykes exhibit a normal magnetic fabric. The orientation of the magnetic lineation k1 axis is more variable than that of the k3 axis, which is generally well grouped. The dykes of São Jorge and São Miguel show a predominance of subhorizontal magmatic flows. In Santa Maria the deduced flow pattern is less systematic changing from subhorizontal in the southern part of the island to oblique in north. These results suggest that the ascent of magma beneath the islands of Azores is predominantly over localized melting sources and then collected within shallow magma chambers. According to this concept, dykes in the upper levels of the crust propagate laterally away from these magma chambers thus feeding the lava flows observed at the surface.

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.