Impact of the background region of interest in the relative renal function

Renal scintigraphy with 99mTc-dimercaptosuccinic acid (99mTc-DMSA) is performed with the aim of detect cortical abnormalities related to urinary tract infection and accurately quantify relative renal function (RRF). For this quantitative assessment Nuclear Medicine Technologist should draw regions of interest (ROI) around each kidney (KROI) and peri-renal background (BKG) ROI, although, controversy still exists about BKG-ROI. The aim of this work was to evaluate the effect of the normalization procedure, number and location of BKG-ROI on the RRF in 99mTc-DMSA scintigraphy.

Relationship of balance to function independence in stroke survivors

Cerebral vascular disease is the primary cause of permanent disability in Portugal. Impaired stability is considered an important feature after stroke as it is related with higher risk of falls and functional dependence. Physiotherapy intervention usually starts early after stroke in order to direct motor recovery and help patients to improve their ability to perform activities of daily living (ADL). Purpose: to investigate the relationship of balance to functionality in acute stroke patients. Methods: 16 subjects (8 women and 8 men), mean age 63,62 ± 2,16y, with unilateral ischemic stroke in the middle cerebral artery territory, who were admitted to physiotherapy department of Fernando Fonseca Hospital in Portugal, within the first month after stroke were recruited to participate in this study. All subjects have no cognitive impairment according to Mini Mental State, no history of lower extremity orthopedic problems and no other disease that could interfere with treatments. All patients gave their inform consent to participate in this study. Subjects were assessed with the Modified Barthel Index (MBI) and the Berg Balance Scale (BBS).

Effects of lower limb strengthening on balance and function in persons who suffered a stroke

Introduction - Cerebrovascular diseases, and among them, cerebral vascular accidents, are one of the main causes of morbidity and disability at European Union countries. Clinical framework resulting from these diseases include important limitations in functional ability of the these patients Postural control dysfunctions are one of the most common and devastating consequences of a stroke interfering with function and autonomy and affecting different aspects of people’s life and contributing to decrease quality of life. Neurological physiotherapy plays a central role in the recovery of movement and posture, however it is necessary to study the efficacy of techniques that physiotherapists use to treat these problems. Objectives - The aim of this study was to investigate the effects of a physiotherapy intervention program, based on oriented tasks and strengthening of the affected lower limb, on balance and functionality of individuals who have suffered a stroke. In addition our study aimed to investigate the effect of strength training of the affected lower limb on muscle tone.

Influence of reconstruction parameters during filtered backprojection and ordered-subset expectation maximization in the measurement of the left-ventricular volumes and function during gated SPECT

A crucial method for investigating patients with coronary artery disease (CAD) is the calculation of the left ventricular ejection fraction (LVEF). It is, consequently, imperative to precisely estimate the value of LVEF--a process that can be done with myocardial perfusion scintigraphy. Therefore, the present study aimed to establish and compare the estimation performance of the quantitative parameters of the reconstruction methods filtered backprojection (FBP) and ordered-subset expectation maximization (OSEM). Methods: A beating-heart phantom with known values of end-diastolic volume, end-systolic volume, and LVEF was used. Quantitative gated SPECT/quantitative perfusion SPECT software was used to obtain these quantitative parameters in a semiautomatic mode. The Butterworth filter was used in FBP, with the cutoff frequencies between 0.2 and 0.8 cycles per pixel combined with the orders of 5, 10, 15, and 20. Sixty-three reconstructions were performed using 2, 4, 6, 8, 10, 12, and 16 OSEM subsets, combined with several iterations: 2, 4, 6, 8, 10, 12, 16, 32, and 64. Results: With FBP, the values of end-diastolic, end-systolic, and the stroke volumes rise as the cutoff frequency increases, whereas the value of LVEF diminishes. This same pattern is verified with the OSEM reconstruction. However, with OSEM there is a more precise estimation of the quantitative parameters, especially with the combinations 2 iterations × 10 subsets and 2 iterations × 12 subsets. Conclusion: The OSEM reconstruction presents better estimations of the quantitative parameters than does FBP. This study recommends the use of 2 iterations with 10 or 12 subsets for OSEM and a cutoff frequency of 0.5 cycles per pixel with the orders 5, 10, or 15 for FBP as the best estimations for the left ventricular volumes and ejection fraction quantification in myocardial perfusion scintigraphy.

Impact of the background Region of Interest in the relative renal function

Renal scintigraphy with 99mTc-dimercaptosuccinic acid (99mTc-DMSA) is performed with the aim of detect cortical abnormalities related to urinary tract infection and accurately quantify relative renal function (RRF). For this quantitative assessment Nuclear Medicine Technologist should draw regions of interest (ROI) around each kidney (KROI) and peri-renal background (BKG) ROI although controversy still exists about BKG-ROI. The aim of this work was to evaluate the effect of the normalization procedure, number and location of BKG-ROI on the RRF in 99mTc-DMSA scintigraphy.

Improved matching criterion for frame rate upconversion with trilateral filtering

Frame rate upconversion (FRUC) is an important post-processing technique to enhance the visual quality of low frame rate video. A major, recent advance in this area is FRUC based on trilateral filtering which novelty mainly derives from the combination of an edge-based motion estimation block matching criterion with the trilateral filter. However, there is still room for improvement, notably towards reducing the size of the uncovered regions in the initial estimated frame, this means the estimated frame before trilateral filtering. In this context, proposed is an improved motion estimation block matching criterion where a combined luminance and edge error metric is weighted according to the motion vector components, notably to regularise the motion field. Experimental results confirm that significant improvements are achieved for the final interpolated frames, reaching PSNR gains up to 2.73 dB, on average, regarding recent alternative solutions, for video content with varied motion characteristics.

Reading performance in children with visual function anomalies

Aims - To compare reading performance in children with and without visual function anomalies and identify the influence of abnormal visual function and other variables in reading ability. Methods - A cross-sectional study was carried in 110 children of school age (6-11 years) with Abnormal Visual Function (AVF) and 562 children with Normal Visual Function (NVF). An orthoptic assessment (visual acuity, ocular alignment, near point of convergence and accommodation, stereopsis and vergences) and autorefraction was carried out. Oral reading was analyzed (list of 34 words). Number of errors, accuracy (percentage of success) and reading speed (words per minute - wpm) were used as reading indicators. Sociodemographic information from parents (n=670) and teachers (n=34) was obtained. Results - Children with AVF had a higher number of errors (AVF=3.00 errors; NVF=1.00 errors; p<0.001), a lower accuracy (AVF=91.18%; NVF=97.06%; p<0.001) and reading speed (AVF=24.71 wpm; NVF=27.39 wpm; p=0.007). Reading speed in the 3rd school grade was not statistically different between the two groups (AVF=31.41 wpm; NVF=32.54 wpm; p=0.113). Children with uncorrected hyperopia (p=0.003) and astigmatism (p=0.019) had worst reading performance. Children in 2nd, 3rd, or 4th grades presented a lower risk of having reading impairment when compared with the 1st grade. Conclusion - Children with AVF had reading impairment in the first school grade. It seems that reading abilities have a wide variation and this disparity lessens in older children. The slow reading characteristics of the children with AVF are similar to dyslexic children, which suggest the need for an eye evaluation before classifying the children as dyslexic.

Logarithmically proportional objective function for planar surfaces recognition in 3D point cloud

3D laser scanning is becoming a standard technology to generate building models of a facility's as-is condition. Since most constructions are constructed upon planar surfaces, recognition of them paves the way for automation of generating building models. This paper introduces a new logarithmically proportional objective function that can be used in both heuristic and metaheuristic (MH) algorithms to discover planar surfaces in a point cloud without exploiting any prior knowledge about those surfaces. It can also adopt itself to the structural density of a scanned construction. In this paper, a metaheuristic method, genetic algorithm (GA), is used to test this introduced objective function on a synthetic point cloud. The results obtained show the proposed method is capable to find all plane configurations of planar surfaces (with a wide variety of sizes) in the point cloud with a minor distance to the actual configurations. © 2014 IEEE.

Visual function assessment in medical imaging research

Background - Medical image perception research relies on visual data to study the diagnostic relationship between observers and medical images. A consistent method to assess visual function for participants in medical imaging research has not been developed and represents a significant gap in existing research. Methods - Three visual assessment factors appropriate to observer studies were identified: visual acuity, contrast sensitivity, and stereopsis. A test was designed for each, and 30 radiography observers (mean age 31.6 years) participated in each test. Results - Mean binocular visual acuity for distance was 20/14 for all observers. The difference between observers who did and did not use corrective lenses was not statistically significant (P = .12). All subjects had a normal value for near visual acuity and stereoacuity. Contrast sensitivity was better than population norms. Conclusion - All observers had normal visual function and could participate in medical imaging visual analysis studies. Protocols of evaluation and populations norms are provided. Further studies are necessary to understand fully the relationship between visual performance on tests and diagnostic accuracy in practice.

Evaluation of lung function abnormalities prevalence in poultry workers

Introduction - Poultry workers can be at an increased risk of occupational respiratory diseases, like asthma, chronic obstructive pulmonary disease and extrinsic allergic alveolitis. Spirometry screening is fundamental to early diagnosis trough the identification of related ventilatory defects. Purpose - We aimed to assess the prevalence of lung function abnormalities in poultry workers.

Light memory function in a double pin SiC device

A double pi'npin heterostructure based on amorphous SiC has a non linear spectral gain which is a function of the signal wavelength that impinges on its front or back surface. An impulse of a configurable length and amplitude is applied to a 390 nm LED which illuminates one of the sensor surfaces, followed by a time period without any illumination after which an input signal with a different wavelength is impinged upon the front surface. Results show that the intensity and duration of the impulse illumination of the surfaces influences the sensor's response with different output for the same input signal. This paper studies this effect and proposes an application as a short term light memory. (C) 2015 Elsevier B.V. All rights reserved.

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.