Voxel

Summary

In 3D computer graphics, a voxel represents a value on a regular grid in three-dimensional space. As with pixels in a 2D bitmap, voxels themselves do not typically have their position (i.e. coordinates) explicitly encoded with their values. Instead, rendering systems infer the position of a voxel based upon its position relative to other voxels (i.e., its position in the data structure that makes up a single volumetric image). In contrast to pixels and voxels, polygons are often explicitly represented by the coordinates of their vertices (as points). A direct consequence of this difference is that polygons can efficiently represent simple 3D structures with much empty or homogeneously filled space, while voxels excel at representing regularly sampled spaces that are non-homogeneously filled. Voxels are frequently used in the visualization and analysis of medical and scientific data (e.g. geographic information systems (GIS)). Some volumetric displays use voxels to describe their

Official source

https://en.wikipedia.org/wiki/Voxel

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Preterm birth is a major pediatric health problem that perturbs the genetically determined program of corticogenesis of the developing brain. As a consequence, prematurity has been strongly associated with adverse long-term neurodevelopmental outcome that may persist even into adulthood. Early characterization of the underlying neuronal mechanisms and early identification of infants at risk is of paramount importance since it allows better development of early therapeutic interventions aiming to prevent adverse outcomes through resilience. This dissertation aims to investigate the consequences of preterm birth on brain function and structure and their relation to adverse neurodevelopmental outcome, as well as to unveil the effect of an early music intervention on brain function. Research to date has mainly focused on the effect of early interventions on the long-term outcome but not on the effect of those interventions on brain function in preterm populations. Moreover, a clear consensus about the predictive utility of MRI volumetric data for long-term outcomes is still missing. This dissertation consists of three main parts. First, we investigate the effects of an early musical intervention in preterm infants using a task-based and resting-state fMRI paradigm. We explore brain effective connectivity changes during task and functional connectivity (FC) modulations at rest. We show that music exposure during hospitalization promotes brain auditory processing maturation. We find increased FC between the auditory cortex and regions involved in familiarity and music processing, such as the thalamus and caudate nucleus. Moreover, we report increased rs-FC in regions involved in associative memory and multisensory processing, such as the calcarine and angular gyrus with a dosage-dependent effect on this modulation. Secondly, we examine brain volumetric development and whether brain volumes are associated with cognitive and behavioural long-term outcomes. We find that perinatal risk factors, such as asphyxia and sepsis have an impact on brain tissue volume. We also find that cognitive and behavioural outcomes are strongly associated with parental SES and that brain tissue volumes at TEA have moderate predictive power. Next, inspired by studies that have characterized the progression of neurodegenerative diseases, we employ an advanced technique to unveil the sequence of biological events along the course of prematurity. Our novel findings might be of clinical use as they allow more targeted early interventions aiming to improve long-term outcomes. Finally, we propose a framework that enables the spectral graph theory analysis of the functional connectome at the voxel level. Our results indicate the presence of both functional gradients and community structure revealing fine-grained connectivity patterns. The proposed framework opens a new avenue for spectral graph theory research at the voxel level.

EPFL2021

Towards a high-density packing white matter substrate generator for Monte-Carlo simulations

Erick Jorge Canales Rodriguez, Rémy Marc M Gardier, Gabriel Girard, Jonathan Rafael Patino Lopez, Jean-Philippe Thiran, Juan Luis Villarreal Haro

One main goal of Diffusion-Weighted Magnetic Resonance Imaging is to infer microscopic tissue properties from the measured signal. The use of Monte-Carlo Simulations for DW-MRI on realistic substrates will help the study of DWI-MRI signals in controlled environments and to investigate, extract, and validate approaches for the understanding of white matter features. In this work, we present a novel framework for creating complex white matter phantoms with unprecedented characteristics like high packing density greater than 90% intra-axonal volume fraction and voxel size of (140 um)3.

2022

White matter hyperintensities (WMH) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMH labour intensive and prone to subjective bias. This study compares fully automated supervised detection methods that learn to identify WMH from manual examples against unsupervised approaches on the combination of FLAIR and T1 weighted images. Data were collected from ten subjects with mild cognitive impairment and another set of ten individuals who fulfilled diagnostic criteria for dementia. Data were split into balanced groups to create a training set used to optimize the different methods. Manual outlining served as gold standard to evaluate performance of the automated methods that identified each voxel either as intact or as part of a WMH. Otsu's approach for multiple thresholds which is based only on voxel intensities of the FLAIR image produced a high number of false positives at grey matter boundaries. Performance on an independent test set was similarly disappointing when simply applying a threshold to the FLAIR that was found from training data. Among the supervised methods, precision-recall curves of support vector machines (SVM) indicated advantages over the performance achieved by K-nearest-neighbor classifiers (KNN). The curves indicated a clear benefit from optimizing the threshold of the SVM decision value and the voting rule of the KNN. Best performance was reached by selecting training voxels according to their distance to the lesion boundary and repeated training after replacing the feature vectors from those voxels that did not form support vectors of the SVM. The study demonstrates advantages of SVM for the problem of detecting WMH at least for studies that include only FLAIR and T1 weighted images. Various optimization strategies are discussed and compared against each other.

Elsevier2011

EPFL2021