955 resultados para Diffusion magnetic resonance Imaging
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The incidence of encephalic tumors in dogs and cats has increased in recent years due to the constant advancement of methods of specialist Diagnostic Imaging: Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), used in small animals. These tools, which were distant in the past, are now becoming increasingly important as an additional aid to the identification of tumor processes in the Central Nervous System. The objective, of the present study, was describe imaging findings obtained in 32 cases of encephalic tumors, through techniques of CT and MR imaging procedures during the years 2004 to 2011. Were diagnosed 19/32 by MRI and 13/32 by CT, being the most affected breed Boxer (9/32), the mean age was 10 years.
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Advanced diagnostic techniques such as magnetic resonance imaging and computed tomography have become useful tools for confirmation of presumptive diagnosis of structural lesions in the brain such as encephalic neoplasms in small animal veterinary practice in Colombia, allowing an effective treatment planning that is more specific and less invasive for this type of pathology.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Objective To assess several baseline risk factors that may predict patellofemoral and tibiofemoral cartilage loss during a 6-month period. Methods For 177 subjects with chronic knee pain, 3T magnetic resonance imaging (MRI) of both knees was performed at baseline and followup. Knees were semiquantitatively assessed, evaluating cartilage morphology, subchondral bone marrow lesions, meniscal morphology/extrusion, synovitis, and effusion. Age, sex, and body mass index (BMI), bone marrow lesions, meniscal damage/extrusion, synovitis, effusion, and prevalent cartilage damage in the same subregion were evaluated as possible risk factors for cartilage loss. Logistic regression models were applied to predict cartilage loss. Models were adjusted for age, sex, treatment, and BMI. Results Seventy-nine subregions (1.6%) showed incident or worsening cartilage damage at followup. None of the demographic risk factors was predictive of future cartilage loss. Predictors of patellofemoral cartilage loss were effusion, with an adjusted odds ratio (OR) of 3.5 (95% confidence interval [95% CI] 1.39.4), and prevalent cartilage damage in the same subregion with an adjusted OR of 4.3 (95% CI 1.314.1). Risk factors for tibiofemoral cartilage loss were baseline meniscal extrusion (adjusted OR 3.6 [95% CI 1.310.1]), prevalent bone marrow lesions (adjusted OR 4.7 [95% CI 1.119.5]), and prevalent cartilage damage (adjusted OR 15.3 [95% CI 4.947.4]). Conclusion Cartilage loss over 6 months is rare, but may be detected semiquantitatively by 3T MRI and is most commonly observed in knees with Kellgren/Lawrence grade 3. Predictors of patellofemoral cartilage loss were effusion and prevalent cartilage damage in the same subregion. Predictors of tibiofemoral cartilage loss were prevalent cartilage damage, bone marrow lesions, and meniscal extrusion.
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Purpose: To assess the correlation between MRI findings of the pancreas with those of the heart and liver in patients with beta thalassemia; to compare the pancreas T2* MRI results with glucose and ferritin levels and labile plasma iron (LPI). Materials and methods: We retrospectively evaluated chronically transfused patients, testing glucose with enzymatic tests, serum ferritin with chemiluminescence, LPI with cellular fluorescence, and T2* MRI to assess iron content in the heart, liver, and pancreas. MRI results were compared with one another and with serum glucose, ferritin, and LPI. Liver iron concentration (LIC) was determined in 11 patients' liver biopsies by atomic absorption spectrometry. Results: 289 MRI studies were available from 115 patients during the period studied. 9.4% of patients had overt diabetes and an additional 16% of patients had impaired fasting glucose. Both pancreatic and cardiac R2* had predictive power (p < 0.0001) for identifying diabetes. Cardiac and pancreatic R2* were modestly correlated with one another (r(2) = 0.20, p < 0.0001). Both were weakly correlated with LIC (r(2) = 0.09, p < 0.0001 for both) and serum ferritin (r(2) = 0.14, p < 0.0001 and r(2) = 0.03, p < 0.02, respectively). None of the three served as a screening tool for single observations. There is a strong log-log, or power-law, relationship between ratio of signal intensity (SIR) values and pancreas R2* with an r(2) of 0.91. Conclusions: Pancreatic iron overload can be assessed by MRI, but siderosis in other organs did not correlate significantly with pancreatic hemosiderosis. (C) 2011 Elsevier Ireland Ltd. All rights reserved.
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Pulmonary arterial hypertension (PAH) is a disease of the pulmonary vasculature characterized by vasoconstriction and vascular remodeling leading to a progressive increase in pulmonary vascular resistance (PVR). It is becoming increasingly recognized that it is the response of the right ventricle (RV) to the increased afterload resulting from this increase in PVR that is the most important determinant of patient outcome. A range of hemodynamic, structural, and functional measures associated with the RV have been found to have prognostic importance in PAH and, therefore, have potential value as parameters for the evaluation and follow-up of patients. If such measures are to be used clinically, there is a need for simple, reproducible, accurate, easy-to-use, and noninvasive methods to assess them. Cardiac magnetic resonance imaging (CMRI) is regarded as the "gold standard" method for assessment of the RV, the complex structure of which makes accurate assessment by 2-dimensional methods, such as echocardiography, challenging. However, the majority of data concerning the use of CMRI in PAH have come from studies evaluating a variety of different measures and using different techniques and protocols, and there is a clear need for the development of standardized methodology if CMRI is to be established in the routine assessment of patients with PAH. Should such standards be developed, it seems likely that CMRI will become an important method for the noninvasive assessment and monitoring of patients with PAH. (C) 2012 Elsevier Inc. All rights reserved. (Am J Cardiol 2012;110[suppl]:25S-31S)
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The autoregressive (AR) estimator, a non-parametric method, is used to analyze functional magnetic resonance imaging (fMRI) data. The same method has been used, with success, in several other time series data analysis. It uses exclusively the available experimental data points to estimate the most plausible power spectra compatible with the experimental data and there is no need to make any assumption about non-measured points. The time series, obtained from fMRI block paradigm data, is analyzed by the AR method to determine the brain active regions involved in the processing of a given stimulus. This method is considerably more reliable than the fast Fourier transform or the parametric methods. The time series corresponding to each image pixel is analyzed using the AR estimator and the corresponding poles are obtained. The pole distribution gives the shape of power spectra, and the pixels with poles at the stimulation frequency are considered as the active regions. The method was applied in simulated and real data, its superiority is shown by the receiver operating characteristic curves which were obtained using the simulated data.
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Purpose: Mossy fiber sprouting (MFS) is a frequent finding following status epilepticus (SE). The present study aimed to test the feasibility of using manganese-enhanced magnetic resonance imaging (MEMRI) to detect MFS in the chronic phase of the well-established pilocarpine (Pilo) rat model of temporal lobe epilepsy (TLE). Methods: To modulate MFS, cycloheximide (CHX), a protein synthesis inhibitor, was coadministered with Pilo in a subgroup of animals. In vivo MEMRI was performed 3 months after induction of SE and compared to the neo-Timm histologic labeling of zinc mossy fiber terminals in the dentate gyrus (DG). Key Findings: Chronically epileptic rats displaying MFS as detected by neo-Timm histology had a hyperintense MEMRI signal in the DG, whereas chronically epileptic animals that did not display MFS had minimal MEMRI signal enhancement compared to nonepileptic control animals. A strong correlation (r = 0.81, p < 0.001) was found between MEMRI signal enhancement and MFS. Significance: This study shows that MEMRI is an attractive noninvasive method for detection of mossy fiber sprouting in vivo and can be used as an evaluation tool in testing therapeutic approaches to manage chronic epilepsy.
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Purpose: To evaluate if the Breast Imaging Reporting and Data System (BI-RADS) ultrasound descriptor of orientation can be used in magnetic resonance imaging (MRI). Materials and Methods: We conducted a retrospective study to evaluate breast mass lesions identified by MRI from 2008 to 2010 who had ultrasound (US) and histopathologic confirmation. Lesions were measured in the craniocaudal (CC), anteroposterior (AP), and transverse (T) axes and classified as having a nonparallel orientation, longest axis perpendicular to Cooper's ligaments, or in a parallel orientation when the longest axis is parallel to Cooper's ligaments. The MR image data were correlated with the US orientation according to BI-RADS and histopathological diagnosis. Results: We evaluated 71 lesions in 64 patients. On MRI, 27 lesions (38.0%) were nonparallel (8 benign and 19 malignant), and 44 lesions (62.0%) were parallel (33 benign and 11 malignant). There was significant agreement between the lesion orientation on US and MRI (kappa value = 0.901). The positive predictive values (PPV) for parallel orientation malignancy on MR and US imaging were 70.4% and 73.1%, respectively. Conclusion: A descriptor of orientation for breast lesions can be used on MRI with PPV for malignant lesions similar to US. J. Magn. Reson. Imaging 2012; 36:13831388. (C) 2012 Wiley Periodicals, Inc.
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OBJECTIVE: To propose an automatic brain tumor segmentation system. METHODS: The system used texture characteristics as its main source of information for segmentation. RESULTS: The mean correct match was 94% of correspondence between the segmented areas and ground truth. CONCLUSION: Final results showed that the proposed system was able to find and delimit tumor areas without requiring any user interaction.