Steren Chabert

Diffusion tensor imaging of the human optic nerve using a non-CPMG fast spin echo sequence

To investigate the diffusion tensor properties of the human optic nerve in vivo using a non‐Carr‐Purcell‐Meiboom‐Gill (CPMG) fast spin echo (FSE) sequence.

Diffusion Signal in Magnetic Resonance Imaging: Origin and Interpretation in Neurosciences

Diffusion Magnetic Resonance Imaging provides images of unquestionable diagnostic value. It is commonly used in the assessment of stroke and in white matter fiber tracking, among other applications. The diffusion coefficient has been shown to depend on cell concentration, membrane permeability, and cell orientation in the case of white matter or muscle fiber tracking; yet a clear relation between diffusion measurements and known physiological parameters is not established. The aim of this paper is to review hypotheses and actual knowledge on diffusion signal origin to provide assistance in the interpretation of diffusion MR images. Focus will be set on brain images, as most common applications of diffusion MRI are found in neuroradiology. Diffusion signal does not come from two intra- or extracellular compartments, as was first assumed. Restriction of water displacement due to membranes, hindrance in the extracellular space, and tissue heterogeneity are important factors. Unanswered questions remain on how to deal with tissue heterogeneity, and how to retrieve parameters less troublesome to work with from biological and clinical points of view. Diffusion quantification should be done with care, as many variables can lead to variation in measurements.

Self-Organizing Neuro-Fuzzy Inference System

The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogerss ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a users performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.

Fuzzy spatial growing for glioblastoma multiforme segmentation on brain magnetic resonance imaging

Image segmentation is a fundamental technique in medical applications. For example, the extraction of biometrical parameter of tumors is of paramount importance both for clinical practice and for clinical studies that evaluate new brain tumor therapies. Tumor segmentation from brain Magnetic Resonance Images (MRI) is a difficult task due to strong signal heterogeneities and weak contrast at the boundary delimitation. In this work we propose a new framework to segment the Glioblastoma Multiforme (GBM) from brain MRI. The proposed algorithm was constructed based on two well known techniques: Region Growing and Fuzzy C-Means. Furthermore, it considers the intricate nature of the GBM in MRI and incorporates a fuzzy formulation of Region Growing with an automatic initialization of the seed points. We report the performance results of our segmentation framework on brain MRI obtained from patients of the chilean Carlos Van Buren Hospital and we compare the results with Region Growing and the classic Fuzzy C-Means approaches.

Normal values of wall shear stress in the pulmonary artery from 4D flow data

m 2 ; RPA = 0.36±0.1N/m 2 ; LPA = 0.28±0.14N/m 2 . Figure 1 shows the average WSS-M calculated along the cardiac cycle for each segment. Figure 2 depicts Bland Altman plots of the mean WSS measured by the two observers, showing a small bias and standard deviations (mean difference of 0.016N/m 2 , 0.008N/m 2 and 0.005N/ m 2 for the WSS-M in the PA, RPA and LPA respectively). Conclusions In this work we proposed a novel and reproducible method to calculate WSS derived from 4D flow data in the main PA, RPA and LPA. In volunteers, we found a greater WSS in the RPA compared with the LPA, which is probably associated with more complex flow patterns (helices) in the RPA (2). Values of WSS obtained in patients showed increasing values of WSS, probably owe to complex and retrograde flow patterns in the pulmonary circulation.

Brain Tumors: How Can Images and Segmentation Techniques Help?

These days, cancer is one of the diseases that scares people the most. Brain cancer may be considered among the most difficult cancers to treat, as it involves the organ which is not only in control of the body, but is also responsible for the self-definition of the person. During surgery or any kind of treatment, eloquent areas must not be affected in order to minimize iatrogenic risks. Therefore good diagnosis and planning of treatment choices is essential. This is why images are now of paramount importance in the evaluation of brain tumors: oncologists, neurosurgeons and the entire medical team need to know how to understand them and how to use the current tools provided by computational techniques to take advantage of the information retrieved from them. A wide variety of images is available to support the physician’s actions at different levels, including diagnosis, treatment election, interventional support, and follow-up. Investigation in this area is very active; attempts are being made to go beyond the current pixel resolution, and to gain information with “molecular images”; not only in nuclear medicine but also in magnetic resonance images. Everybody agrees that images are now an invaluable service in the practice of medicine. However, the present and future use of images is intrinsically associated with larger numbers of images, which are not easily manageable by either radiologists or surgeons. Neuroradiology is conceived as a discipline in which the health status of a patient is inferred according to the visual inspection of images taken from different modalities. This implies that the success of the clinical diagnosis depends on the physician’s particular skills, and also on the information that the clinical team can handle. In addition, numerous image modalities are used frequently at different time points; therefore there is also a need for integration of the features reflected by these different sources of images. In order to provide support for this integration, automatic processing methods have been developed. Many Computer Aided Diagnostic (CAD) software packages have been developed, in particular to provide second readings in mammography, lung or brain cancer (Doi, 2007). These developments have motivated several clinical applications. Regarding brain tumor image processing, what is usually expected is to detect the localization and extension of the tumor, in other words to segment the tumor in the image.

Pseudorehearsal Approach for Incremental Learning of Deep Convolutional Neural Networks

Deep Convolutional Neural Networks, like most connectionist models, suffers from catastrophic forgetting while training for a new, unknown task. One of the simplest solutions to this issue is adding samples of previous data, with the drawback of increasingly having to store training data; or generating patterns that evoke similar responses of the previous task.

Multiple echo multi-shot diffusion sequence.

To measure both transversal relaxation time (T2) and diffusion coefficients within a single scan using a multi‐shot approach. Both measurements have drawn interest in many applications, especially in skeletal muscle studies, which have short T2 values. Multiple echo single‐shot schemes have been proposed to obtain those variables simultaneously within a single scan, resulting in a reduction of the scanning time. However, one problem with those approaches is the associated long echo read‐out. Consequently, the minimum achievable echo time tends to be long, limiting the application of these sequences to tissues with relatively long T2.

Quantitative description of the morphology and ossification center in the axial skeleton of 20‐week gestation formalin‐fixed human fetuses using magnetic resonance images

Human tissues are usually studied using a series of two‐dimensional visualizations of in vivo or cutout specimens. However, there is no precise anatomical description of some of the processes of human fetal development. The purpose of our study is to develop a quantitative description of the normal axial skeleton by means of high‐resolution three‐dimensional magnetic resonance (MR) images, collected from six normal 20‐week‐old human fetuses fixed in formaldehyde.

Análisis cuantitativo de variables hemodinámicas de la aorta obtenidas de 4D flow

Quantitative analysis of hemodynamic variables of the aorta by 4D flow MRI Abstract. Objective: Hemodynamic parameters are critical to perform a proper diagnosis. However, due to the large number of variables that can be obtained, overall analysis may represent a complex task. To facilitate this, we propose to create a model for classifying different hemodynamic variables belonging to both healthy individuals and pathological patients. For this purpose, we employed data mining techniques to identify relationships among various aortic hemodynamic parameters obtained through multi-dimensional (4D flow) MR imaging. Method: A 4D-flow sequence of whole heart and great vessels was acquired using MRI in 19 healthy volunteers and 2 patients (one with aortic coarctation and one with repaired coarctation of the aorta). Retrospectively, data was reformatted along the aorta; three MRI acquisitions were performed for volunteers and 30 sequences for each patient. In each slice the aorta was segmented and various parameters were quantified: area, maximum velocity, minimum velocity, flow and volume, with following values being calculated for last four parameters: maximum, average, standard deviation, kurtosis, bias, proportion of time to reach the maximum value, among others. A total of 26 variables for each acquisition were obtained. In order to classify data, the CART Technique (Classification and Regression Trees) was applied. To validate the model, two extra projections were generated per each volunteer and 20 slices per each patient. Results: By using only 7 variables, the CART technique allows discrimination between images performed either on volunteers or patients with an error rate of 14.1%, a sensitivity of 82.5%, and a specificity of 89.4%. Conclusions: 4D-flow MR imaging provides a wealth of hemodynamic data that can be difficult to analyze. In this paper we demonstrate that by using data mining techniques it is possible to classify images from relevant hemodynamic parameters and their relationships in order to support the diagnosis of cardiovascular disorders.