[Response prediction for chronic migraine preventive treatment by gray matter morphometry in magnetic resonance imaging: a pilot study]. / Predicción de la respuesta al tratamiento preventivo en migraña crónica mediante la medición de la sustancia gris en resonancia magnética: estudio piloto.

INTRODUCTION: Topiramate is the only oral preventative with level of evidence I for the treatment of chronic migraine. AIM: To evaluate gray matter parameters, obtained with magnetic resonance imaging (MRI), as biomarkers of the response to topiramate in chronic migraine patients. PATIENTS AND METHODS: The sample was composed by 57 chronic migraine patients, screened for first time in a Headache Unit due to chronic migraine. MRI acquisitions were performed at a 3 T unit. Afterwards, topiramate preventive treatment began. Response and tolerability were evaluated after three months, defining response as at least 50% reduction in headache days per month. We included patients that tolerated topiramate. T1- and diffusion-weighted MRI were processed to obtain gray matter (68 cortical and 16 subcortical regions) descriptive parameters. A logistic regression model was employed for the predictive assessment. RESULTS: Forty-two patients tolerated the treatment and were analyzed, responding 23 of them (54.7%). The final prediction model was built with gray matter parameters with significant results. In this model, higher left cuneus curvature and right insula area values were associated with a higher probability of response, while higher right inferior parietal cortex volume and left superior temporal gyrus area values were associated with a lower probability. The accuracy of the predictive model was 95%. CONCLUSION: The gray matter parameters may be useful biomarkers of preventive treatment response with topiramate in chronic migraine.

TITLE: Predicción de la respuesta al tratamiento preventivo en migraña crónica mediante la medición de la sustancia gris en resonancia magnética: estudio piloto.Introducción. El topiramato es el único tratamiento preventivo oral con nivel de evidencia I para la migraña crónica. Objetivo. Evaluar los parámetros de la sustancia gris, obtenidos mediante resonancia magnética, como marcadores de respuesta al tratamiento con topiramato en pacientes con migraña crónica. Pacientes y métodos. La muestra se compuso de 57 pacientes con migraña crónica atendidos por primera vez en una unidad de cefaleas como consecuencia de migraña crónica, a los que se realizó una resonancia magnética de 3 T. Posteriormente, se inició el tratamiento preventivo con topiramato. Se evaluaron la respuesta y la tolerancia a los tres meses y se definió respuesta como disminución de al menos un 50% en el número de días de cefalea al mes. Mediante procesamiento de imágenes de resonancia magnética ponderadas en T1 y difusión, se obtuvieron los parámetros de la sustancia gris (68 estructuras corticales y 16 subcorticales). Se obtuvo un modelo de regresión logística para la valoración predictiva. Resultados. Se analizó a 42 pacientes que toleraron el tratamiento, con respuesta terapéutica en 23 de ellos (54,7%). El modelo final de predicción se construyó con parámetros de la sustancia gris con resultados significativos. En dicho modelo, a mayor curvatura del cúneo izquierdo y área de la ínsula derecha, mayor probabilidad de respuesta, y menor probabilidad a mayor volumen de la corteza inferior parietal derecha y área del giro temporal superior izquierdo. La precisión del modelo predictivo fue del 95%. Conclusión. Los parámetros de la sustancia gris pueden ser marcadores útiles de respuesta al tratamiento preventivo con topiramato en la migraña crónica.

Probabilistic-driven oriented Speckle reducing anisotropic diffusion with application to cardiac ultrasonic images.

A novel anisotropic diffusion filter is proposed in this work with application to cardiac ultrasonic images. It includes probabilistic models which describe the probability density function (PDF) of tissues and adapts the diffusion tensor to the image iteratively. For this purpose, a preliminary study is performed in order to select the probability models that best fit the stastitical behavior of each tissue class in cardiac ultrasonic images. Then, the parameters of the diffusion tensor are defined taking into account the statistical properties of the image at each voxel. When the structure tensor of the probability of belonging to each tissue is included in the diffusion tensor definition, a better boundaries estimates can be obtained instead of calculating directly the boundaries from the image. This is the main contribution of this work. Additionally, the proposed method follows the statistical properties of the image in each iteration. This is considered as a second contribution since state-of-the-art methods suppose that noise or statistical properties of the image do not change during the filter process.

Signal LMMSE estimation from multiple samples in MRI and DT-MRI.

A method to estimate the magnitude MR data from several noisy samples is presented. It is based on the Linear Minimum Mean Squared Error (LMMSE) estimator for the Rician noise model when several scanning repetitions are available. This method gives a closed-form analytical solution that takes into account the probability distribution of the data as well as the existing level of noise, showing a better performance than methods such as the average or the median.