Revisión sistemática : uso de imágenes por difusión en RMN, para predecir sobrevida en pacientes adultos con diagnostico de glioblastoma multiforme

El Glioblastoma multiforme (GBM), es el tumor cerebral más frecuente, con pronóstico grave y baja sensibilidad al tratamiento inicial. El propósito de este estudio fue evaluar si la Difusión en RM (IDRM), es un biomarcador temprano de respuesta tumoral, útil para tomar decisiones tempranas de tratamiento y para obtener información pronostica. Metodología La búsqueda se realizo en las bases de datos EMBASE, CENTRAL, MEDLINE; las bibliografías también fueron revisadas. Los artículos seleccionados fueron estudios observacionales (casos y controles, cohortes, corte transversal), no se encontró ningún ensayo clínico; todos los participante tenían diagnostico histopatológico de GBM, sometidos a resección quirúrgica y/o radio-quimioterapia y seguimiento de respuesta al tratamiento con IDRM por al menos 6 meses. Los datos extraídos de forma independiente fueron tipo de estudio, participantes, intervenciones, seguimiento, desenlaces (sobrevida, progresión/estabilización de la enfermedad, muerte) Resultados Quince estudios cumplieron los criterios de inclusión. Entre las técnicas empleadas de IDRM para evaluar respuesta radiológica al tratamiento, fueron histogramas del coeficiente aparente de difusion ADC (compararon valores inferiores a la media y el percentil 10 de ADC, con los valores superiores); encontrando en términos generales que un ADC bajo es un fuerte predictor de sobrevida y/o progresión del tumor. (Esto fue significativo en 5 estudios); mapas funcionales de difusion (FDM) (midieron el porcentaje de cambio de ADC basal vs pos tratamiento) que mostro ser un fuerte predictor de sobrevida en pacientes con progresión tumoral. DISCUSION Desafortunadamente la calidad de los estudios fue intermedia-baja lo que hace que la aplicabilidad de los estudios sea limitada.

Resonancia magnética cardiaca: medidas lineales y volumétricas en adultos sanos

Introducción: La gran mayoría de las medidas de normalidad utilizadas para la interpretación de resonancia cardiaca son extrapoladas de las medidas de ecocardiografía. Los limitados registros de medidas de normalidad se encuentran ajustados en poblaciones extranjeras, no hay registros en latinoamericanos. Objetivo: Determinar las dimensiones cardiacas utilizando resonancia magnética en una población de personas sin antecedente médicos con repercusión cardiaca para lograr una muestra de valores que permitan ajustar las medidas de normalidad utilizadas por nuestro servicio. Materiales y métodos: se analizaron 45 sujetos sanos con edad comprendida entre los 21 y 45 años, las adquisiciones se realizaron utilizando un equipo de RM de 1,5 teslas, el análisis de las imágenes se realizó mediante el programa Cardiac Volume Vx. Se evaluaron múltiples parámetros morfofuncionales a través de análisis estadístico por medio del sistema SPSS versión 23. Resultados: Mediciones obtenidas de ventrículo izquierdo principales fueron volumen diastólico en mujeres de 62 ml +/- 7.1 y en hombres de 65 ml +/- 11.2 y fracción de eyección de 60 % +/- 5 en mujeres y de 62 % +/- 9 en hombres. En ventrículo derecho el volumen diastólico final se encontró 81.8 ml +/- 14.6 en mujeres y 100 ml +/- 24.8 en hombres y fracción de eyección de 53 % +/- 17 en mujeres y de 45 % +/- 12 en hombres. Volumen de fin de diástole de 50 +/- 12.7 ml en mujeres y de 49 ml +/- 19 ml en hombres y fracción de eyección de aurícula izquierda de 55 % +/- 0.08 en mujeres y de 50 % +/- 0.07 en hombres. Volumen de fin de diástole de 44.1 ml +/- 18.5 en mujeres y de 49.2 ml +/- 22.9 en hombres y fracción de eyección de aurícula derecha de 50 % +/- 11 en mujeres y de 45 % +/- 8 en hombres. Se obtuvieron otras medidas lineales y volumétricas adicionales de cavidades cardiacas y de grandes vasos supracardiacos. Conclusiones: se describen los valores de referencia de los parámetros morfofuncionales de las cavidades cardiacas y de vasos supracardiacos. El sexo fue tenido en cuenta como covariable relacionada con la modificación de los parámetros evaluados. Se sugieren variaciones en las medidas de cavidades cardiacas para la población estudiada relacionada con aclimatación crónica a la altitud de la ciudad de Bogotá.

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Visualitzation and processing of diffusion tensor MRI

Diffusion Tensor Imaging (DTI) is a new magnetic resonance imaging modality capable of producing quantitative maps of microscopic natural displacements of water molecules that occur in brain tissues as part of the physical diffusion process. This technique has become a powerful tool in the investigation of brain structure and function because it allows for in vivo measurements of white matter fiber orientation. The application of DTI in clinical practice requires specialized processing and visualization techniques to extract and represent acquired information in a comprehensible manner. Tracking techniques are used to infer patterns of continuity in the brain by following in a step-wise mode the path of a set of particles dropped into a vector field. In this way, white matter fiber maps can be obtained.

Imagem por ressonância magnética da vasculatura da pele humana : viabilidade no contexto clínico

Imagiologia por Ressonância Magnética (IRM) é uma modalidade de imagem médica que está a recuperar o interesse como uma técnica não invasiva no estudo da pele. Tipicamente campos magnéticos de elevada densidade e quipamentos específicos são usados. Este facto limita o usos da técnica a laboratórios e centros de investigação especializados. Neste trabalho estudou-se a viabilidade do uso da IRM no estudo da pele e da sua vasculatura usando equipamento convencional disponível em contexto clínico. Sequências IRM para imagem estrutural e veascular foram optimizadas e testadas para obtenção de imagens da pele do punho de 6 voluntários saudáveis. As sequências observáveis dos vasos, razão sinal-ruído, e razão contraste-ruído. Foi observado que duas sequências volumétricas baseadas em eco de gradiente e com ponderações T1 e T2 forneciam informação complementar em respeito à vasculatura da pele com resoluções espaciais da ordem dos micrómetros, podendo ainda esta informação ser fundida com imagens estruturais das cadamas da pele. Foi igualmente observado que estas sequências fornecem informação útil usando equipamento convencional e perspectiva-se a sua utilização no estudo das vasculatura de tumores cutâneos e na doença vascular periférica.

Case Study: Interacting with Volumetric Medical Datasets in Networked CAVE Environments

Virtual Reality (VR) is widely used in visualizing medical datasets. This interest has emerged due to the usefulness of its techniques and features. Such features include immersion, collaboration, and interactivity. In a medical visualization context, immersion is important, because it allows users to interact directly and closelywith detailed structures in medical datasets. Collaboration on the other hand is beneficial, because it gives medical practitioners the chance to share their expertise and offer feedback and advice in a more effective and intuitive approach. Interactivity is crucial in medical visualization and simulation systems, because responsiveand instantaneous actions are key attributes in applications, such as surgical simulations. In this paper we present a case study that investigates the use of VR in a collaborative networked CAVE environment from a medical volumetric visualization perspective. The study will present a networked CAVE application, which has been built to visualize and interact with volumetric datasets. We will summarize the advantages of such an application and the potential benefits of our system. We also will describe the aspects related to this application area and the relevant issues of such implementations.

Clustered functional MRI of overt speech production

To investigate the neural network of overt speech production, eventrelated fMRI was performed in 9 young healthy adult volunteers. A clustered image acquisition technique was chosen to minimize speechrelated movement artifacts. Functional images were acquired during the production of oral movements and of speech of increasing complexity (isolated vowel as well as monosyllabic and trisyllabic utterances). This imaging technique and behavioral task enabled depiction of the articulo-phonologic network of speech production from the supplementary motor area at the cranial end to the red nucleus at the caudal end. Speaking a single vowel and performing simple oral movements involved very similar activation of the corticaland subcortical motor systems. More complex, polysyllabic utterances were associated with additional activation in the bilateral cerebellum,reflecting increased demand on speech motor control, and additional activation in the bilateral temporal cortex, reflecting the stronger involvement of phonologic processing.

ATD: a multiplatform for semiautomatic 3-D detection of kidneys and their pathology in real time

This research presents a novel multi-functional system for medical Imaging-enabled Assistive Diagnosis (IAD). Although the IAD demonstrator has focused on abdominal images and supports the clinical diagnosis of kidneys using CT/MRI imaging, it can be adapted to work on image delineation, annotation and 3D real-size volumetric modelling of other organ structures such as the brain, spine, etc. The IAD provides advanced real-time 3D visualisation and measurements with fully automated functionalities as developed in two stages. In the first stage, via the clinically driven user interface, specialist clinicians use CT/MRI imaging datasets to accurately delineate and annotate the kidneys and their possible abnormalities, thus creating “3D Golden Standard Models”. Based on these models, in the second stage, clinical support staff i.e. medical technicians interactively define model-based rules and parameters for the integrated “Automatic Recognition Framework” to achieve results which are closest to that of the clinicians. These specific rules and parameters are stored in “Templates” and can later be used by any clinician to automatically identify organ structures i.e. kidneys and their possible abnormalities. The system also supports the transmission of these “Templates” to another expert for a second opinion. A 3D model of the body, the organs and their possible pathology with real metrics is also integrated. The automatic functionality was tested on eleven MRI datasets (comprising of 286 images) and the 3D models were validated by comparing them with the metrics from the corresponding “3D Golden Standard Models”. The system provides metrics for the evaluation of the results, in terms of Accuracy, Precision, Sensitivity, Specificity and Dice Similarity Coefficient (DSC) so as to enable benchmarking of its performance. The first IAD prototype has produced promising results as its performance accuracy based on the most widely deployed evaluation metric, DSC, yields 97% for the recognition of kidneys and 96% for their abnormalities; whilst across all the above evaluation metrics its performance ranges between 96% and 100%. Further development of the IAD system is in progress to extend and evaluate its clinical diagnostic support capability through development and integration of additional algorithms to offer fully computer-aided identification of other organs and their abnormalities based on CT/MRI/Ultra-sound Imaging.

Advanced feature selection methods in multinominal dementia classification from structural MRI data

Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification.

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge.