916 resultados para Diffusional kurtosis imaging
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Diffusion Kurtosis Imaging (DKI) is a fairly new magnetic resonance imag-ing (MRI) technique that tackles the non-gaussian motion of water in biological tissues by taking into account the restrictions imposed by tissue microstructure, which are not considered in Diffusion Tensor Imaging (DTI), where the water diffusion is considered purely gaussian. As a result DKI provides more accurate information on biological structures and is able to detect important abnormalities which are not visible in standard DTI analysis. This work regards the development of a tool for DKI computation to be implemented as an OsiriX plugin. Thus, as OsiriX runs under Mac OS X, the pro-gram is written in Objective-C and also makes use of Apple’s Cocoa framework. The whole program is developed in the Xcode integrated development environ-ment (IDE). The plugin implements a fast heuristic constrained linear least squares al-gorithm (CLLS-H) for estimating the diffusion and kurtosis tensors, and offers the user the possibility to choose which maps are to be generated for not only standard DTI quantities such as Mean Diffusion (MD), Radial Diffusion (RD), Axial Diffusion (AD) and Fractional Anisotropy (FA), but also DKI metrics, Mean Kurtosis (MK), Radial Kurtosis (RK) and Axial Kurtosis (AK).The plugin was subjected to both a qualitative and a semi-quantitative analysis which yielded convincing results. A more accurate validation pro-cess is still being developed, after which, and with some few minor adjust-ments the plugin shall become a valid option for DKI computation
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Mestrado em Radiações Aplicadas às Tecnologias da Saúde. Área de especialização: Ressonância Magnética
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Structural connectivity models based on Diffusion Tensor Imaging (DTI) are strongly affected by the technique’s inability to resolve crossing fibres, either intra- or inter-hemispherical connections. Several models have been proposed to address this issue, including an algorithm aiming to resolve crossing fibres which is based on Diffusion Kurtosis Imaging (DKI). This technique is clinically feasible, even when multi-band acquisitions are not available, and compatible with multi-shell acquisition schemes. DKI is an extension of DTI enabling the estimation of diffusion tensor and diffusion kurtosis metrics. In this study we compare the performance of DKI and DTI in performing structural brain connectivity. Six healthy subjects were recruited, aged between 25 and 35 (three females). The MRI experiments were performed using a 3T Siemens Trio with a 32-channel head coil. The scans included a T1-weighted sequence (1mm3), and a DWI with b-values 0, 1000 and 2000 s:mm
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La quantificazione non invasiva delle caratteristiche microstrutturali del cervello, utilizzando la diffusion MRI (dMRI), è diventato un campo sempre più interessante e complesso negli ultimi due decenni. Attualmente la dMRI è l’unica tecnica che permette di sondare le proprietà diffusive dell’acqua, in vivo, grazie alla quale è possibile inferire informazioni su scala mesoscopica, scala in cui si manifestano le prime alterazioni di malattie neurodegenerative, da tale tipo di dettaglio è potenzialmente possibile sviluppare dei biomarcatori specifici per le fasi iniziali di malattie neurodegenerative. L’evoluzione hardware degli scanner clinici, hanno permesso lo sviluppo di modelli di dMRI avanzati basati su acquisizioni multi shell, i quali permettono di ovviare alle limitazioni della Diffusion Tensor Imaging, in particolare tali modelli permettono una migliore ricostruzione trattografica dei fasci di sostanza bianca, grazie ad un’accurata stima della Orientation Distribution Function e la stima quantitativa di parametri che hanno permesso di raggiungere una miglior comprensione della microstruttura della sostanza bianca e delle sue eventuali deviazioni dalla norma. L’identificazione di biomarcatori sensibili alle prime alterazioni microstrutturali delle malattie neurodegenerative è uno degli obbiettivi principali di tali modelli, in quanto consentirebbero una diagnosi precoce e di conseguenza un trattamento terapeutico tempestivo prima di una significante perdità cellulare. La trattazione è suddivisa in una prima parte di descrizione delle nozioni fisiche di base della dMRI, dell’imaging del tensore di diffusione e le relative limitazioni, ed in una seconda parte dove sono analizzati tre modelli avanzati di dMRI: Diffusion Kurtosis Imaging, Neurite Orientation Dispersion and Density Imaging e Multi Shell Multi Tissue Constrained Spherical Deconvolution. L'obiettivo della trattazione è quello di offrire una panoramica sulle potenzialità di tali modelli.
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In schizophrenic psychoses, structural and functional alterations of the amygdala have been demonstrated by several neuroimaging studies. However, postmortem examinations on the brains of schizophrenics did not confirm the volume changes reported by volumetric magnetic resonance imaging (MRI) studies. In order to address these contradictory findings and to further elucidate the possibly underlying pathophysiological process of the amygdala, we employed a trimodal MRI design including high-resolution volumetry, diffusion tensor imaging (DTI), and quantitative magnetization transfer imaging (qMTI) in a sample of 14 schizophrenic patients and 14 matched controls. Three-dimensional MRI volumetry revealed a significant reduction of amygdala raw volumes in the patient group, while amygdala volumes normalized for intracranial volume did not differ between the two groups. The regional diffusional anisotropy of the amygdala, expressed as inter-voxel coherence (COH), showed a marked and significant reduction in schizophrenics. Assessment of qMTI parameters yielded significant group differences for the T2 time of the bound proton pool and the T1 time of the free proton pool, while the semi-quantitative magnetization transfer ratio (MTR) did not differ between the groups. The application of multimodal MRI protocols is diagnostically relevant for the differentiation between schizophrenic patients and controls and provides a new strategy for the detection and characterization of subtle structural alterations in defined regions of the living brain.
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In recent years observations at the level of individual atoms and molecules became possible by microscopy and spectroscopy. Imaging of single fluorescence molecules has been achieved but has so far been restricted to molecules in the immobile state. Here we provide methodology for visualization of the motion of individual fluorescent molecules. It is applied to imaging of the diffusional path of single molecules in a phospholipid membrane by using phospholipids carrying one rhodamine dye molecule. For this methodology, fluorescence microscopy was carried to a sensitivity so that single fluorescent molecules illuminated for only 5 ms were resolvable at a signal/noise ratio of 28. Repeated illuminations permitted direct observation of the diffusional motion of individual molecules with a positional accuracy of 30 nm. Such capability has fascinating potentials in bioscience--for example, to correlate biological functions of cell membranes with movements, spatial organization, and stoichiometries of individual components.
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Split-plot design (SPD) and near-infrared chemical imaging were used to study the homogeneity of the drug paracetamol loaded in films and prepared from mixtures of the biocompatible polymers hydroxypropyl methylcellulose, polyvinylpyrrolidone, and polyethyleneglycol. The study was split into two parts: a partial least-squares (PLS) model was developed for a pixel-to-pixel quantification of the drug loaded into films. Afterwards, a SPD was developed to study the influence of the polymeric composition of films and the two process conditions related to their preparation (percentage of the drug in the formulations and curing temperature) on the homogeneity of the drug dispersed in the polymeric matrix. Chemical images of each formulation of the SPD were obtained by pixel-to-pixel predictions of the drug using the PLS model of the first part, and macropixel analyses were performed for each image to obtain the y-responses (homogeneity parameter). The design was modeled using PLS regression, allowing only the most relevant factors to remain in the final model. The interpretation of the SPD was enhanced by utilizing the orthogonal PLS algorithm, where the y-orthogonal variations in the design were separated from the y-correlated variation.
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The aim of this study is to test the feasibility and reproducibility of diffusion-weighted magnetic resonance imaging (DW-MRI) evaluations of the fetal brains in cases of twin-twin transfusion syndrome (TTTS). From May 2011 to June 2012, 24 patients with severe TTTS underwent MRI scans for evaluation of the fetal brains. Datasets were analyzed offline on axial DW images and apparent diffusion coefficient (ADC) maps by two radiologists. The subjective evaluation was described as the absence or presence of water diffusion restriction. The objective evaluation was performed by the placement of 20-mm(2) circular regions of interest on the DW image and ADC maps. Subjective interobserver agreement was assessed by the kappa correlation coefficient. Objective intraobserver and interobserver agreements were assessed by proportionate Bland-Altman tests. Seventy-four DW-MRI scans were performed. Sixty of them (81.1%) were considered to be of good quality. Agreement between the radiologists was 100% for the absence or presence of diffusion restriction of water. For both intraobserver and interobserver agreement of ADC measurements, proportionate Bland-Altman tests showed average percentage differences of less than 1.5% and 95% CI of less than 18% for all sites evaluated. Our data demonstrate that DW-MRI evaluation of the fetal brain in TTTS is feasible and reproducible.
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The present essay is illustrated with magnetic resonance images obtained at the authors' institution over the past 15 years and discusses the main imaging findings of intraventricular tumor-like lesions (ependymoma, pilocytic astrocytoma, central neurocytoma, ganglioglioma, choroid plexus papilloma, primitive neuroectodermal tumors, meningioma, epidermoid tumor). Such lesions represent a subgroup of intracranial lesions with unique characteristics and some image patterns that may facilitate the differential diagnosis.
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The present essay is illustrated with magnetic resonance images obtained at the authors' institution over the past 15 years and discusses the main imaging findings of intraventricular tumor-like lesions (colloid cyst, oligodendroglioma, astroblastoma, lipoma, cavernoma) and of inflammatory/infectious lesions (neurocysticercosis and an atypical presentation of neurohistoplasmosis). Such lesions represent a subgroup of intracranial lesions with unique characteristics and some imaging patterns that may facilitate the differential diagnosis.
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Chronic pain has been often associated with myofascial pain syndrome (MPS), which is determined by myofascial trigger points (MTrP). New features have been tested for MTrP diagnosis. The aim of this study was to evaluate two-dimensional ultrasonography (2D US) and ultrasound elastography (UE) images and elastograms of upper trapezius MTrP during electroacupuncture (EA) and acupuncture (AC) treatment. 24 women participated, aged between 20 and 40 years (M ± SD = 27.33 ± 5.05) with a body mass index ranging from 18.03 to 27.59 kg/m2 (22.59 ± 3.11), a regular menstrual cycle, at least one active MTrP at both right (RTPz) and left trapezius (LTPz) and local or referred pain for up to six months. Subjects were randomized into EA and AC treatment groups and the control sham AC (SHAM) group. Intensity of pain was assessed by visual analogue scale; MTrP mean area and strain ratio (SR) by 2D US and UE. A significant decrease of intensity in general, RTPz, and LTPz pain was observed in the EA group (p = 0.027; p < 0.001; p = 0.005, respectively) and in general pain in the AC group (p < 0.001). Decreased MTrP area in RTPz and LTPz were observed in AC (p < 0.001) and EA groups (RTPz, p = 0.003; LTPz, p = 0.005). Post-treatment SR in RTPz and LTPz was lower than pre-treatment in both treatment groups. 2D US and UE effectively characterized MTrP and surrounding tissue, pointing to the possibility of objective confirmation of subjective EA and AC treatment effects.
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The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50μgcm(-2).
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A method using the ring-oven technique for pre-concentration in filter paper discs and near infrared hyperspectral imaging is proposed to identify four detergent and dispersant additives, and to determine their concentration in gasoline. Different approaches were used to select the best image data processing in order to gather the relevant spectral information. This was attained by selecting the pixels of the region of interest (ROI), using a pre-calculated threshold value of the PCA scores arranged as histograms, to select the spectra set; summing up the selected spectra to achieve representativeness; and compensating for the superimposed filter paper spectral information, also supported by scores histograms for each individual sample. The best classification model was achieved using linear discriminant analysis and genetic algorithm (LDA/GA), whose correct classification rate in the external validation set was 92%. Previous classification of the type of additive present in the gasoline is necessary to define the PLS model required for its quantitative determination. Considering that two of the additives studied present high spectral similarity, a PLS regression model was constructed to predict their content in gasoline, while two additional models were used for the remaining additives. The results for the external validation of these regression models showed a mean percentage error of prediction varying from 5 to 15%.
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