NABS: non-local automatic brain hemisphere segmentation

"NOTICE: this is the author’s version of a work that was accepted for publication in Magnetic Resonance Imaging. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Magnetic Resonance Imaging, [Volume 33, Issue 4, May 2015, Pages 474–484] DOI 10.1016/j.mri.2015.02.005In this paper, we propose an automatic method to segment the five main brain sub-regions (i.e. left/right hemispheres, left/right cerebellum and brainstem) from magnetic resonance images. The proposed method uses a library of pre-labeled brain images in a stereotactic space in combination with a non-local label fusion scheme for segmentation. The main novelty of the proposed method is the use of a multi-label block-wise label fusion strategy specifically designed to deal with the classification of main brain sub-volumes that process only specific parts of the brain images significantly reducing the computational burden. The proposed method has been quantitatively evaluated against manual segmentations. The evaluation showed that the proposed method was faster while producing more accurate segmentations than a current state-of-the-art method. We also present evidences suggesting that the proposed method was more robust against brain pathologies than the compared method. Finally, we demonstrate the clinical value of our method compared to the state-of-the-art approach in terms of the asymmetry quantification in Alzheimer's disease.We want to thank the OASIS (P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584) and IXI - Information eXtraction from Images (EPSRC GR/S21533/02) datasets promoters for making available this valuable resource to the scientific community which surely will boost the research in brain imaging. This work has been supported by the Spanish grant TIN2011-26727 from Ministerio de Ciencia e Innovacion. J. Tohka's work was supported by the Academy of Finland grant 130275. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the Future Programme IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of Excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57).Romero Gómez, JE.; Manjón Herrera, JV.; Tohka, J.; Coupé, P.; Robles Viejo, M. (2015). NABS: non-local automatic brain hemisphere segmentation. Magnetic Resonance Imaging. 33(4):474-484. https://doi.org/10.1016/j.mri.2015.02.005S47448433

The Human Connectome Project's neuroimaging approach

Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Adaptive microstructure-informed tractography for accurate brain connectivity analyses

Human brain has been subject of deep interest for centuries, given it's central role in controlling and directing the actions and functions of the body as response to external stimuli. The neural tissue is primarily constituted of neurons and, together with dendrites and the nerve synapses, constitute the gray matter (GM) which plays a major role in cognitive functions. The information processed in the GM travel from one region to the other of the brain along nerve cell projections, called axons. All together they constitute the white matter (WM) whose wiring organization still remains challenging to uncover. The relationship between structure organization of the brain and function has been deeply investigated on humans and animals based on the assumption that the anatomic architecture determine the network dynamics. In response to that, many different imaging techniques raised, among which diffusion-weighted magnetic resonance imaging (DW-MRI) has triggered tremendous hopes and expectations. Diffusion-weighted imaging measures both restricted and unrestricted diffusion, i.e. the degree of movement freedom of the water molecules, allowing to map the tissue fiber architecture in vivo and non-invasively. Based on DW-MRI data, tractography is able to exploit information of the local fiber orientation to recover global fiber pathways, called streamlines, that represent groups of axons. This, in turn, allows to infer the WM structural connectivity, becoming widely used in many different clinical applications as for diagnoses, virtual dissections and surgical planning. However, despite this unique and compelling ability, data acquisition still suffers from technical limitations and recent studies have highlighted the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. The focus of this Ph.D. project is to specifically address these limitations and to improve the anatomical accuracy of the structural connectivity estimates. To this aim, we developed a global optimization algorithm that exploits micro and macro-structure information, introducing an iterative procedure that uses the underlying tissue properties to drive the reconstruction using a semi-global approach. Then, we investigated the possibility to dynamically adapt the position of a set of candidate streamlines while embedding the anatomical prior of trajectories smoothness and adapting the configuration based on the observed data. Finally, we introduced the concept of bundle-o-graphy by implementing a method to model groups of streamlines based on the concept that axons are organized into fascicles, adapting their shape and extent based on the underlying microstructure

Tractographie adaptative basée sur la microstructure pour des analyses précises de la connectivité cérébrale

Le cerveau est un sujet de recherche depuis plusieurs décennies, puisque son rôle est central dans la compréhension du genre humain. Le cerveau est composé de neurones, où leurs dendrites et synapses se retrouvent dans la matière grise alors que les axones en constituent la matière blanche. L’information traitée dans les différentes régions de la matière grise est ensuite transmise par l’intermédiaire des axones afin d’accomplir différentes fonctions cognitives. La matière blanche forme une structure d’interconnections complexe encore dif- ficile à comprendre et à étudier. La relation entre l’architecture et la fonction du cerveau a été étudiée chez les humains ainsi que pour d’autres espèces, croyant que l’architecture des axones déterminait la dynamique du réseau fonctionnel. Dans ce même objectif, l’Imagerie par résonance (IRM) est un outil formidable qui nous permet de visualiser les tissus cérébraux de façon non-invasive. Plus partic- ulièrement, l’IRM de diffusion permet d’estimer et de séparer la diffusion libre de celle restreinte par la structure des tissus. Cette mesure de restriction peut être utilisée afin d’inférer l’orientation locale des faisceaux de matière blanche. L’algorithme de tractographie exploite cette carte d’orientation pour reconstruire plusieurs connexions de la matière blanche (nommées “streamlines”). Cette modélisation de la matière blanche permet d’estimer la connectivité cérébrale dite structurelle entre les différentes régions du cerveau. Ces résultats peuvent être employés directement pour la planification chirurgicale ou indirectement pour l’analyse ou une évaluation clinique. Malgré plusieurs de ses limitations, telles que sa variabilité et son imprécision, la tractographie reste l’unique moyen d’étudier l’architecture de la matière blanche ainsi que la connectivité cérébrale de façon non invasive. L’objectif de ce projet de doctorat est de répondre spécifiquement à ces limitations et d’améliorer la précision anatomique des estimations de connectivité structurelle. Dans ce but, nous avons développé un algorithme d’optimisation globale qui exploite les informations de micro et macrostructure, en introduisant une procédure itéra- tive qui utilise les propriétés sous-jacentes des tissus pour piloter la reconstruction en utilisant une approche semi-globale. Ensuite, nous avons étudié la possibilité d’adapter dynamiquement la position d’un ensemble de lignes de courant candidates tout en intégrant le préalable anatomique de la douceur des trajectoires et en adap- tant la configuration en fonction des données observées. Enfin, nous avons introduit le concept de bundle-o-graphy en mettant en œuvre une méthode pour modéliser des groupes de lignes de courant basées sur le concept que les axones sont organisés en fascicules, en adaptant leur forme et leur étendue en fonction de la microstructure sous-jacente.Abstract : Human brain has been subject of deep interest for centuries, given it’s central role in controlling and directing the actions and functions of the body as response to external stimuli. The neural tissue is primarily constituted of neurons and, together with dendrites and the nerve synapses, constitute the gray matter (GM) which plays a major role in cognitive functions. The information processed in the GM travel from one region to the other of the brain along nerve cell projections, called axons. All together they constitute the white matter (WM) whose wiring organization still remains challenging to uncover. The relationship between structure organization of the brain and function has been deeply investigated on humans and animals based on the assumption that the anatomic architecture determine the network dynamics. In response to that, many different imaging techniques raised, among which diffusion-weighted magnetic resonance imaging (DW-MRI) has triggered tremendous hopes and expectations. Diffusion-weighted imaging measures both restricted and unrestricted diffusion, i.e. the degree of movement freedom of the water molecules, allowing to map the tissue fiber architecture in vivo and non-invasively. Based on DW-MRI data, tractography is able to exploit information of the local fiber orien- tation to recover global fiber pathways, called streamlines, that represent groups of axons. This, in turn, allows to infer the WM structural connectivity, becoming widely used in many different clinical applications as for diagnoses, virtual dissections and surgical planning. However, despite this unique and compelling ability, data acqui- sition still suffers from technical limitations and recent studies have highlighted the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. The focus of this Ph.D. project is to specifically address these limitations and to improve the anatomical accuracy of the structural connectivity estimates. To this aim, we developed a global optimization algorithm that exploits micro and macro- structure information, introducing an iterative procedure that uses the underlying tissue properties to drive the reconstruction using a semi-global approach. Then, we investigated the possibility to dynamically adapt the position of a set of candidate streamlines while embedding the anatomical prior of trajectories smoothness and adapting the configuration based on the observed data. Finally, we introduced the concept of bundle-o-graphy by implementing a method to model groups of streamlines based on the concept that axons are organized into fascicles, adapting their shape and extent based on the underlying microstructure.Sommario : Il cervello umano è oggetto di profondo interesse da secoli, dato il suo ruolo centrale nel controllare e dirigere le azioni e le funzioni del corpo in risposta a stimoli esterno. Il tessuto neurale è costituito principalmente da neuroni che, insieme ai dendriti e alle sinapsi nervose, costituiscono la materia grigia (GM), la quale riveste un ruolo centrale nelle funzioni cognitive. Le informazioni processate nella GM viaggiano da una regione all’altra del cervello lungo estensioni delle cellule nervose, chiamate assoni. Tutti insieme costituiscono la materia bianca (WM) la cui organizzazione strutturale rimane tuttora sconosciuta. Il legame tra struttura e funzione del cervello sono stati studiati a fondo su esseri umani e animali partendo dal presupposto che l’architettura anatomica determini la dinamica della rete funzionale. In risposta a ciò, sono emerse diverse tecniche di imaging, tra cui la risonanza magnetica pesata per diffusione (DW-MRI) ha suscitato enormi speranze e aspettative. Questa tecnica misura la diffusione sia libera che ristretta, ovvero il grado di libertà di movimento delle molecole d’acqua, consentendo di mappare l’architettura delle fibre neuronali in vivo e in maniera non invasiva. Basata su dati DW-MRI, la trattografia è in grado di sfruttare le informazioni sull’orientamento locale delle fibre per ricostruirne i percorsi a livello globale. Questo, a sua volta, consente di estrarre la connettività strutturale della WM, utilizzata in diverse applicazioni cliniche come per diagnosi, dissezioni virtuali e pianificazione chirurgica. Tuttavia, nonostante questa capacità unica e promettente, l’acquisizione dei dati soffre ancora di limitazioni tecniche e recenti studi hanno messo in evidenza la scarsa accuratezza anatomica delle ricostruzioni ottenute con questa tecnica, mettendone in dubbio l’efficacia per lo studio della connettività cerebrale. Il focus di questo progetto di dottorato è quello di affrontare in modo specifico queste limitazioni e di migliorare l’accuratezza anatomica delle stime di connettività strutturale. A tal fine, abbiamo sviluppato un algoritmo di ottimizzazione globale che sfrutta le informazioni sia micro che macrostrutturali, introducendo una procedura iterativa che utilizza le proprietà del tessuto neuronale per guidare la ricostruzione utilizzando un approccio semi-globale. Successivamente, abbiamo studiato la possibilità di adattare dinamicamente la posizione di un insieme di streamline candidate incorporando il prior anatomico per cui devono seguire traiettorie regolari e adattando la configurazione in base ai dati osservati. Infine, abbiamo introdotto il concetto di bundle-o-graphy implementando un metodo per modellare gruppi di streamline basato sul concetto che gli assoni sono organizzati in fasci, adattando la loro forma ed estensione in base alla microstruttura sottostante

Preprocessing methods for morphometric brain analysis and quality assurance of structural magnetic resonance images

Gegenstand der Dissertation ist die Neuentwicklung und Validierung von Verfahren zur Aufbereitung von anatomischen Daten, die mittels Magnetresonanztomographie gewonnen wurden. Ziel ist dabei die Erfassung von morphometrischen Kennwerten zur Beschreibung der Struktur und Form des Gehirns, wie beispielsweise Volumen, Fläche, Dicke oder Faltung der Großhirnrinde. Die Kennwerte erlauben sowohl die Erforschung individueller gesunder und pathologischer Entwicklung als auch der evolutionären Anpassung des Gehirns. Die zur Datenanalyse notwendige Vorverarbeitung beinhaltet dabei die Angleichung von Bildeigenschaften und individueller Anatomie. Die fortlaufende Weiterentwicklung der Scanner- und Rechentechnik ermöglicht eine zunehmend genauere Bildgebung, erfordert aber die kontinuierliche Anpassung existierender Verfahren. Die Schwerpunkte dieser Dissertation lagen in der Entwicklung neuer Verfahren zur (i) Klassifikation der Hirngewebe (Segmentierung), (ii) räumlichen Abbildung des individuellen Gehirns auf ein Durchschnittsgehirn (Registrierung), (iii) Bestimmung der Dicke der Großhirnrinde und Rekonstruktion einer repräsentativen Oberfläche und (iv) Qualitätssicherung der Eingangsdaten. Die Segmentierung gleicht die Bildeigenschaften unterschiedlicher Protokolle an, während die Registrierung anatomische Merkmale normalisiert und so den Vergleich verschiedener Gehirne ermöglicht. Die Rekonstruktion von Oberflächen erlaubt wiederum die Gewinnung einer Vielzahl weiterer morphometrischer Maße zur spezifischen Charakterisierung des Gehirns und seiner Entwicklung. Anhand von simulierten und realen Daten wird die Validität der neuen Methoden belegt und mit anderen Ansätzen verglichen. Die Verfahren sind Bestandteil der Computational Anatomy Toolbox (CAT; http://dbm.neuro.uni-jena.de/cat), deren Schwerpunkt die Vorverarbeitung von strukturellen Daten ist und die Teil des Statistical Parametric Mapping (SPM) Softwarepaketes in MATLAB ist.This Ph.D. thesis focuses on the development, optimization and validation of preprocessing methods of structural magnetic resonance images of the brain. The preprocessing describes the creation of morphometric data that support a statistical analysis of brain anatomy. Image interferences have to be removed to allow a tissue classification (segmentation). In order to compare different subjects a spatial normalization to an average-shaped brain (template) is required, where atlas maps allow identification of specific brain structures and regions of interest. Beside the analysis in a voxel-grid, the cortex can be represented by surfaces that allow further measures such as the cortical thickness or folding. The derived brain features (such as volume, area, and thickness) permit the individual study of normal and pathological development during the lifespan but also of the evolutionary adaption of the brain. The ongoing progress of imaging and computing technology demands continous enhancement of preprocessing tools but also facilitates the exploration of novel approaches and models. The basis of this thesis is the development of a method that uses a tissue segmentation to estimate the cortical thickness and the central surface in one integrated step. Further essential improvements of surface reconstruction algorithms were achieved by specific refinement of processing steps such as (i) the classification of brain tissue (segmentation), (ii) the spatial mapping of the individual brain to an average brain (registration), (iii) determining the thickness of the cerebral cortex and reconstructing a representative surface and (iv) the quality assurance of input data. The validity of the new methods is proven and compared with other approaches by simulated and real data. The procedures are part of the Computational Anatomy Toolbox (CAT; http://dbm.neuro.uni-jena.de/cat), which focuses on the preprocessing of structural data and is part of the Statistical Parametric Mapping (SPM) software package in MATLAB