1,721,174 research outputs found
Advanced image-processing techniques in magnetic resonance imaging for the investigation of brain pathologies and tumour angiogenesis
Full text linkL'imaging a risonanza magnetica (MRI) è sempre più utilizzato in ambiente medico per la sua abilità di produrre in modo non invasivo immagini di altà qualità dell'interno del corpo umano. Sin dalla sua introduzione nei primi anni 70, techiche di acquisizione via via più complesse sono state proposte, portando l'MRI ad essere utilizzata su uno spettro di applicazioni sempre più ampio. Le tecniche più innovative, tra cui la risonanza magnetica funzionale e di diffusione, richiedono tecniche di analisi ed algoritmi di elaborazione molto complessi per estrarre informazioni utili dai dati acquisiti. Lo scopo di questa tesi è stato quello di sviluppare e ottimizzare tecniche avanzate di elaborazione per applicarle all'analisi di dati di risonanza magnetica sia in ambiente preclinico che clinico. Durante il corso di dottorato sono stato coinvolto attivamente in diversi progetti di ricerca, ed ogni volta mi sono trovato ad affrontare problematiche diverse. In questa tesi, tuttavia, saranno riportati i risultati ottenuti nei tre progetti più interessanti a cui ho preso parte.
Tali progetti avevano come obiettivo (i) l'implementazione di un protocollo sperimentale innovativo per imaging funzionale in animali da laboratorio, (ii) lo sviluppo di nuovi metodi per l'analisi di dati di Dynamic Contrast Enhanced MRI in modelli sperimentali di tumore e (iii) l'analisi di dati di diffusione in pazienti affetti da ischemia cerebrale. Particolare enfasi sarà posta sugli aspetti tecnici che riguardano gli algoritmi ed i metodi di elaborazione utilizzati nel processo di analisi.Magnetic resonance imaging (MRI) is increasingly being used in medical settings because of its ability to produce, non-invasively, high quality images of the inside of the human body. Since its introduction in early 70’s, more and more complex acquisition techniques have been proposed, raising MRI to be exploited in a wide spectrum of applications. Innovative MRI modalities, such as diffusion and functional imaging, require complex analysis techniques and advanced algorithms in order to extract useful information from the acquired data.
The aim of the present work has been to develop and optimize state-of-the-art techniques to be applied in the analysis of MRI data both in experimental and clinical settings. During my doctoral program I have been actively involved in several research projects, each time facing many different issues. In this dissertation, however, I will report the results obtained in three most appealing projects I partecipated to. These projects were devoted (i) to the implementation of an innovative experimental protocol for functional MRI in laboratory animals, (ii) to the development of new methods for the analysis of Dynamic Contrast Enhanced MRI data in experimental tumour models and (iii) to the analysis of diffusion MRI data in stroke patients. Particular emphasis will be given to the technical aspects regarding the algorithms and processing methods used in the analysis of data
Microstructure informed tractography with anatomical priors
Full text linkIt is known from brain anatomy that axons in the white matter are structured and organized in groups, called fascicles or bundles. Historically, these white matter structures have been studied in detail using invasive techniques, such as dissection, with the aim to elucidate their organization and possible effects of neurological diseases on brain connectivity. With the advent of magnetic resonance imaging, which is a noninvasive acquisition modality, today it is possible to perform "virtual dissections" using tractography. In fact, this technique allows estimating in vivo the macroscopic trajectory of these fascicles using 3D polylines called streamlines, and this unique ability opened a number of exciting possibilities to study the anatomy of the brain noninvasively and to characterize alteration in its structure due to pathology. However, tractography is not perfect and some limitations have recently been pointed out in many studies and received a lot of attention in the scientific community. One of the major problems is the high number of invalid white matter structures reconstructed, i.e., false-positive connections between cortical and subcortical regions, which were shown to potentially bias the characterization of brain connectivity. The main objective of this thesis is to investigate and propose new strategies to improve the anatomical accuracy of tractography. In particular, we extended a microstructure informed tractography framework by introducing the important anatomical prior that axons are organized in groups and using this information as a constraint to reduce ambiguities in the reconstructions. We tested different solutions and showed that it is possible to improve the specificity of connectivity estimates without affecting their sensitivity, both in numerical simulations and real brain data. We implemented a pipeline using a supervised tractography segmentation tool to extract bundles and evaluate the performance of our proposals using in vivo data. In the first proposal, we introduced the possibility of using the definition of groups of streamlines as a constraint in the optimization problem, which produces a big improvement in the connectivity generated with the reconstructions. Later in the second strategy, we extended the framework to use as a constraint a multilevel hierarchical organization of streamlines, which is able to reproduce the connectivity results removing implausible streamlines that are inside the groups that create the connections. Finally, we proposed a robust augmentation to the cost function of the framework that adjusts for the reliability of the original measurements. With the research performed in this thesis, we proved that adding information on the organization of the white matter benefits reconstructions made with tractography. Our proposals represent an additional step forward to improve the anatomical accuracy of tractography and our understanding of how different brain regions are interconnected
Brain tissue segmentation on Diffusion Weighted Magnetic Resonance data
No full text<p>Esteban, Oscar; Gorthi, Subrahmanyam; Daducci, Alessandro; Ledesma Carbayo, María Jesús; Santos Lleo, Andres de; Thiran, Jean-Philippe y Bach-Cuadra, Meritxell (2012). Brain tissue segmentation on Diffusion Weighted Magnetic Resonance data. En: "2012 10th IEEE International Symposium on Biomedical Imaging (ISBI)", 2012, Barcelona (Spain).</p
Analysis of GFA changes along the subcortical motor network after stroke
No full textIn this work we aim at investigating the 3D Simple Harmonic Oscillator based Reconstruction and Estimation3 (3D-SHORE) derived numerical biomarkers for tractometry. In particular, we target the subcortical motor network (SC-MN) of a healthy subject. Using diffusion spectrum imaging (DSI) we reconstructed the SC-MN and compared the resulting information about white matter (WM) density and structure with that provided by Generalized Fractional Anisotropy (GFA) and Magnetization Transfer Ratio (MTR) imaging. The SC-MN gathers the connections between the cortical motor area, the basal ganglia and the thalamus, and it essentially consists of three major subcortical networks (Figure 1): i) the sensory-motor sub-loop (primary and sensory motor areas - putamen- globus pallidus - ventral lateral thalamic nucleus - motor cortex), ii) the premotor sub-loop (premotor dorsal and ventral areas (dPM, vPM) - caudate nucleus - putamen, globus pallidus –ventral anterior thalamic nuleus - premotor cortex iii) the supplementary motor area (SMA) sub-loop (SMA - putamen and caudatus - globus pallidus - ventral anterior and the ventro - lateral thalamic nuclei - SMA)
Diffusion MRI characterization of stroke lesions using 3D-SHORE microstructural indices
No full textRecently, several reconstruction methods of diffusion MRI signal have been introduced in order to overcome Diffusion Tensor Imaging (DTI) limitations. One of these models is the 3D Simple Harmonic Oscillator based Reconstruction and Estimation1 (3D-SHORE) which enables the reconstruction of fiber crossings via the calculation of the Orientation Distribution Function (ODF). From 3D-SHORE it is also possible to derive new diffusion indices such as the Return To the Origin Probability (RTOP), Return To the Axis Probability (RTAP), and Return To the Plane Probability (RTPP)1, in addition to well-established Fractional Anisotropy (FA), and Mean Diffusivity (MD)
Are SHORE-based biomarkers suitable descriptors for microstructure in DSI?
No full textMicro-structural indexes based on a novel reconstruction method for diffusion MRI data (SHORE)1 have recently been
proposed. Nevertheless, such numerical biomarkers require proper validation and so far, no attempt has been done to apply the
SHORE method to human magnetic resonance imaging (MRI) data. In this work, we derive and analyze SHORE descriptors on a group
of human Diffusion Spectrum Imaging (DSI) data. The study aimed at determining their descriptive power, reliability and relations with
established indexes of connectivity microstructure
Tractometry of the subcortical motor network using SHORE- based indices
No full textIntroduction. In this work we investigated the 3D-SHORE numerical indices for quantitative tractography in the sub-cortical
motor network (SC-MN). Using DSI we reconstructed the network connections and compared the outcomes about white
matter (WM) density and structure to those provided by GFA and MTR.
Methods. Ten healthy subjects (age 56.1±17.8 years old, mean±SD) went through a DSI scan twice one month apart (±
1 week, tp1c and tp2c, see [1] for more details). The Ensemble Average Propagator (EAP) was reconstructed using the
SHORE model and the orientational (ODF) and microstructural indices were derived including Return to zero (RTOP),
Return to axis (RTAP) and Return to plan (RTPP) probability and propagator anisotropy (PA). These provide an estimation
of the mean pore geometry (volume, cross-section, length, diameter), irrespectively of the pore shape. SHORE indices
were extracted for each fiber bundle
Neuronal fiber--tracking via optimal mass transportation
No full textDiffusion Magnetic Resonance Imaging (MRI) is used to (non-invasively) study neuronal fibers in the brain white matter. Reconstructing fiber paths from such data (tractography problem) is relevant in particular to study the connectivity between two given cerebral regions. By considering the fiber paths between two given areas as geodesics of a suitable well-posed optimal control problem (related to optimal mass transportation) which takes into account the whole information given by the DDF, we are able to provide a quantitative criterion to estimate the connectivity between two given cerebral regions, and to recover the actual distribution of neuronal fibers between them
Blurred streamlines: A novel representation to reduce redundancy in tractography
No full textTractography is a powerful tool to study brain connectivity in vivo, but it is well known to suffer from an intrinsic trade-off between sensitivity and specificity. A critical – but usually underrated – parameter to choose that can heavily impact the quality of the estimates is the number of streamlines to be reconstructed for a given data set. In fact, sensitivity can be improved by generating more and more streamlines, as all real anatomical connections are likely reconstructed, but lots of false positives are inevitably introduced, too. Consequently, so-called tractography filtering techniques have become increasingly popular to get rid of these false positives and improve specificity. However, increasing number of streamlines introduces redundancy in tractography reconstructions, which may negatively impact the performance of filtering algorithms, especially those based on linear formulations. To address this problem, we introduce a novel streamlines representation, called “blurred streamlines”, which drastically reduces the redundancy among streamlines by (i) clustering similar trajectories and (ii) spatially blurring the corresponding signal contributions. We tested the effectiveness of the blurred streamlines both on synthetic and in vivo data. Our results clearly show that this new representation is as accurate as state-of-the-art methods despite using only 5% of the input streamlines, thus significantly decreasing the computational complexity of filtering algorithms as well as storage requirements of the resulting reconstructions
Bundle-o-graphy: improving structural connectivity estimation with adaptive microstructure-informed tractography
Full text linkTractography is a powerful tool for the investigation of the complex organization of the brain in vivo, as it allows inferring the macroscopic pathways of the major fiber bundles of the white matter based on non-invasive diffusion-weighted magnetic resonance imaging acquisitions. Despite this unique and compelling ability, some studies have exposed the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. In this work, we describe a novel method to readdress tractography reconstruction problem in a global manner by combining the strengths of so-called generative and discriminative strategies. Starting from an input tractogram, we parameterize the connections between brain regions following a bundle-based representation that allows to drastically reducing the number of parameters needed to model groups of fascicles. The parameters space is explored following an MCMC generative approach, while a discrimininative method is exploited to globally evaluate the set of connections which is updated according to Bayes' rule. Our results on both synthetic and real brain data show that the proposed solution, called bundle-o-graphy, allows improving the anatomical accuracy of the reconstructions while keeping the computational complexity similar to other state-of-the-art methods
- …
