Analysis of encephalic lesions of different natures in magnetic resonance and computed tomography images from self-organizing maps

Orientadores: Fabiano Reis, Li Li MinTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências MédicasResumo: Com o advento de conjunto de dados cada vez maiores (Big Data), dentre os quais se incluem imagens médicas com crescente qualidade de resoluções espacial, espectral e radiométrica e, portanto, com maior número de pixels, espectros de varredura e níveis de cinza, faz-se útil o uso de técnicas matemáticas avançadas, especialmente as não-supervisionadas, para aprimorar a segmentação do tecido cerebral em agrupamentos (clusters) distintos, possibilitando uma melhor visualização da área acometida por patologia. Este trabalho teve por objetivo segmentar imagens de sistema nervoso central (SNC) em patologias de três naturezas distintas - neoplásica (tumores), desmielinizante (esclerose múltipla) e vascular (acidente vascular cerebral isquêmico -AVCi). Foram realizados três estudos, descritos nos Artigos I, II e III, nos quais foram analisadas imagens de SNC de pacientes (ressonância magnética - RM, nos Artigos I e II, e tomografia computadorizada - TC, no artigo III) com diagnóstico de, respectivamente, neoplasia, esclerose múltipla tipo remitente-recorrente e AVCi. As imagens foram transformadas em matrizes com valores da escala de cinza para cada pixel, em cada aquisição, e processadas por ferramentas com capacidade de execução de Mapas Auto-Organizáveis (SOM), SiroSOM e Weka, que permitiram a construção de mapa neural com treinamento de neurônios e, posteriormente, particionamento dos mesmos em agrupamentos por K-Médias. As novas matrizes, com assimilação de clusters para cada pixel, foram novamente reconstruídas em imagens, que foram submetidas à avaliação de médicos com formação consolidada prévia em neurorradiologia. Os trabalhos I e II confirmam a capacidade geral de segmentação de imagens médicas por meio de SOM com razoável precisão de delimitação de bordas em RM. O artigo III revelou um grau insatisfatório de exatidão de delineação de bordas de lesões à segmentação de TC, porém com potencial identificável de melhora da acurácia se novos e mais amplos estudos forem realizados, com base no material publicadoAbstract: With the upcoming of ever bigger datasets (Big Data), among those medical images, ever-growing on spatial, spectral and radiometric resolutions and, hence, in the number of pixels, spectrums and digital numbers (DNs), the use of advanced mathematical algorithms, especially non-supervised neural networks, plays a role on improving automated segmentation of the human brain into smaller, distinct clusters, providing a better visualization of the comprised, pathological regions. This work aimed to segment central nervous system (CNS) images from pathologies of three different natures - neoplasms, demyelinating and vascular (ischemic stroke). We performed three studies, each one described in distinct articles, I, II and III, on which medical CNS images (MRI for Articles I and II and CT for article III) from patients with confirmed diagnosis of, respectively, neoplasms, relapsing-remitting multiple sclerosis and ischemic stroke were analyzed. The images were transformed in matrices of gray values for each pixel, in each acquisition, and then processed by Kohonen's Self Organizing Maps (SOM) via capable software - SiroSOM or Weka, through training of neurons belonging to the built neural map, followed by clustering by K Means. The newly created matrices with cluster values for each pixel were then rebuilt back to new images that were appreciated by physicians skilled in neuroradiology. The research confirms the general ability of medical image segmentation by SOM, with reasonable border delimitation in MRI, in articles I and II. Article III revealed a non-satisfactory precision of lesion border delineation on CT, but there is a likely chance of improvement of accuracy, if further, deeper studies based on the publication data could be performedDoutoradoNeurologiaDoutor em Ciências Médicas88881.132052/2016-01CAPE

Improving nonlinear search with Self-Organizing Maps - Application to Magnetic Resonance Relaxometry

Quantification of myelin in vivo is crucial for the understanding of neurological diseases, like multiple sclerosis (MS). Multi-Component Driven Equilibrium Single Pulse Observation T1 and T2 (mcDESPOT) is a rapid and precise method for determination of the longitudinal and transverse relaxation times in a voxel wise fashion. Briefly, mcDESPOT couples sets of SPGR (spoiled gradient-recalled echo) and bSSFP (fully balance steady-state free precession) data acquired over a range of flip angles (α) with constant interpulse spacing (TR) to derive 6 parameters (free-water T1 and T2, myelin-associated water T1 and T2, relative myelin-associated water volume fraction, and the myelin-associated water proton residence time) based on water exchange models. However, this procedure is computationally expensive and extremely difficult due to the need to find the best fit to the 24 MRI signals volumes in a search of nonlinear 6 dimensional space of model parameters. In this context, the aim of this work is to improve mcDESPOT efficiency and accuracy using tissue information contained in the sets of signals (SPGR and bSSFP) acquired. The basic hypothesis is that similar acquired signals are referred to tissue portions with close features, which translate in similar parameters. This similarity could be used to drive the nonlinear mcDESPOT fitting, leading the optimization algorithm (that is based on a stochastic region contraction approach) to look for a solution (i.e. the 6 parameters vector) also in regions defined by previously computed solutions of others voxels with similar signals. For this reason, we clustered the sets of SPGR and bSSFP using the neural network called Self Organizing Map (SOM), which uses a competitive learning technique to train itself in an unsupervised manner. The similarity information obtained from the SOM was then used to accordingly suggest solutions to the optimization algorithm. A first validation phase with in silico data was performed to evaluate the performances of the SOM and of the modified method, SOM+mcDESPOT. The latter was further validated using real magnetic resonance images. The last step consisted of applying the SOM+mcDESPOT to a group of healthy subjects ( ) and a group of MS patients ( ) to look for differences in myelin-associated water fractions values between the two groups. The validation phases with in silico data verified the initial hypothesis: in more the 74% of the times, the correct solution of a certain voxel is in the space dictated by the cluster which that voxel is mapped to. Adding the information of similar solutions extracted from that cluster helps to improve the signals fitting and the accuracy in the determination of the 7 parameters. This result is still present even if the data are corrupted by a high level of noise (SNR=50). Using real images allowed to confirm the power of SOM+mcDESPOT underlined through the in silico data. The application of SOM+mcDESPOT to the controls and to the MS patients allowed firstly obtaining more feasible results than the traditional mcDESPOT. Moreover, a statistically significant difference of the myelin-associated water fraction values in the normal appearing white matter was found between the two groups: the MS patients, in fact, show lower fraction values compared to the normal subjects, indicating an abnormal presence of myelin in the normal appearing white matter of MS patients. In conclusion, we proposed the novel method SOM+mcDESPOT that is able to extract and exploit the information contained in the MRI signals to drive appropriately the optimization algorithm implemented in mcDESPOT. In so doing, the overall accuracy of the method in both the signals fitting and in the determination of the 7 parameters improves. Thus, the outstanding potentiality of SOM+mcDESPOT could assume a crucial role in improving the indirect quantification of myelin in both healthy subjects and patient

Networks of microstructural damage predict disability in multiple sclerosis

Background: Network-based measures are emerging MRI markers in multiple sclerosis (MS). We aimed to identify networks of white (WM) and grey matter (GM) damage that predict disability progression and cognitive worsening using data-driven methods. // Methods: We analysed data from 1836 participants with different MS phenotypes (843 in a discovery cohort and 842 in a replication cohort). We calculated standardised T1-weighted/T2-weighted (sT1w/T2w) ratio maps in brain GM and WM, and applied spatial independent component analysis to identify networks of covarying microstructural damage. Clinical outcomes were Expanded Disability Status Scale worsening confirmed at 24 weeks (24-week confirmed disability progression (CDP)) and time to cognitive worsening assessed by the Symbol Digit Modalities Test (SDMT). We used Cox proportional hazard models to calculate predictive value of network measures. // Results: We identified 8 WM and 7 GM sT1w/T2w networks (of regional covariation in sT1w/T2w measures) in both cohorts. Network loading represents the degree of covariation in regional T1/T2 ratio within a given network. The loading factor in the anterior corona radiata and temporo-parieto-frontal components were associated with higher risks of developing CDP both in the discovery (HR=0.85, p<0.05 and HR=0.83, p<0.05, respectively) and replication cohorts (HR=0.84, p<0.05 and HR=0.80, p<0.005, respectively). The decreasing or increasing loading factor in the arcuate fasciculus, corpus callosum, deep GM, cortico-cerebellar patterns and lesion load were associated with a higher risk of developing SDMT worsening both in the discovery (HR=0.82, p<0.01; HR=0.87, p<0.05; HR=0.75, p<0.001; HR=0.86, p<0.05 and HR=1.27, p<0.0001) and replication cohorts (HR=0.82, p<0.005; HR=0.73, p<0.0001; HR=0.80, p<0.005; HR=0.85, p<0.01 and HR=1.26, p<0.0001). // Conclusions: GM and WM networks of microstructural changes predict disability and cognitive worsening in MS. Our approach may be used to identify patients at greater risk of disability worsening and stratify cohorts in treatment trials

MRI quantification of multiple sclerosis pathology

Background: Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease and a common cause of neurologic disability. MS pathology is characterized by demyelination, neuroaxonal loss and atrophy. Magnetic Resonance Imaging (MRI) is an essential tool in diagnosing and monitoring MS, but its clinical value could be even further expanded by more advanced and quantitative MRI methods, which may also provide additional pathophysiological insights. Purpose: The overall aim of this thesis was to quantify MS pathology using volumetric brain MRI, ultra-high field brain and cervical spinal cord MRI as well as a newly developed rapid myelin imaging technique in relation to cognitive and physical MS disability. Study I, a prospective 17-year longitudinal study of 37 MS participants with 23 age/sex- matched healthy controls for comparison at the last follow-up. Longitudinal volumetric brain 1.5 Tesla MRI during the second half of the study showed that lesion accumulation and corpus callosum atrophy were the most strongly associated neuroanatomical correlates of cognitive disability, with the lesion fraction being an independent predictor of cognitive performance 8.5 years later. Study II, a prospective cross-sectional study of 35 MS participants and 11 age-matched healthy controls using 3 and 7 Tesla MRI. The study demonstrated involvement of both grey and white matter in MS, not only the brain but also the cervical spinal cord, associated with MS disability. Lesions appeared in proximity to the cerebrospinal fluid (CSF), with special predilection to the periventricular and grey matter surrounding the central canal in secondary progressive MS. Study III, a prospective in vivo (71 MS participants and 21 age/sex-matched healthy controls) and ex vivo (brain tissue from 3 MS donors) study at 3 Tesla, showed that a new clinically approved and feasible rapid myelin imaging technique correlates well with myelin stainings and produces robust in vivo myelin quantification that is related to both current and future cognitive and physical disability in MS. Study IV, an in-depth topographical analysis based on Study III, mapped the distribution of demyelination, both in vivo and ex vivo, in the periventricular and perilesional regions of the brain. A gradient of demyelination with predominance near the CSF spaces was seen. Measures of clinical disability were consistently and more strongly associated with the myelin content in normal-appearing tissue compared to the intralesional myelin content. Conclusions: Lesions and atrophy contribute to cognitive and physical disability in MS but to a varying degree, likely dependent on the relative involvement of white vs. grey matter. Both focal lesions/demyelination as well as diffuse demyelination in normal-appearing white matter shows an apparent gradient from the CSF, which differ between relapsing-remitting and progressive MS subtypes/phases. The growing utility and clinical availability of advanced and quantitative MRI techniques holds promise for improved monitoring of MS pathology and likely represents a vital tool for assessing the efficacy of potential remyelinating/reparative therapies in MS

Cerebellar contribution to Cognitive Impairment in early stages of Relapsing-Remitting Multiple Sclerosis: a conventional and rs-fMRI study

Background. The cerebellum is a primary site of Multiple Sclerosis (MS) pathology. Structural and functional MRI studies have demonstrated the role of the posterior cerebellum in cognitive functions. To date, the “Cerebellar Cognitive Affective Syndrome” (CCAS) scale has never been used to test MS-related Cognitive Impairment (CI) and its association with cerebellar involvement. Objectives. We investigated the association of MRI structural and functional abnormalities of the cognitive cerebellum with CI and tested the role of the CCAS scale in detecting CI in a cohort of very early RRMS patients. Methods. 37 patients with early RRMS and 4 age- and sex-matched healthy controls (HC) were enrolled in this cross-sectional, exploratory study. Cognitive performances were assessed through BICAMS, D-KEFS ST, and CCAS scale. Using a CCAS scale score cut-off (based on a 50 HC sample), 26/37 (70%) patients were classified as “Normal-CCAS” and 11/37 (30%) as “Impaired-CCAS”. All subjects underwent a conventional and resting-state functional MRI (rs-fMRI) protocol. Comparisons between groups were assessed for structural and functional MRI parameters. Moreover, correlations between cognitive test scores and structural-functional MRI parameters were evaluated. Results. Patients with pathological score on CCAS also showed CVLT-II and D-KEFS ST low scores. A significant reduction in cerebellar volumetric parameters was found in the CCAS-impaired MS group compared to the normal one, albeit whole brain WM and thalamic volumes were also significantly reduced. The rs-fMRI analysis revealed higher functional connectivity (FC) between the cognitive cerebellum and most of the functional brain cortical networks in the CCAS-impaired group compared to the normal one. Conclusions. Our findings suggest that CI in early RRMS is associated with pathological alterations in both structural and functional MRI parameters. Higher FC between cerebellar-brain networks in CCAS-impaired patients might be the expression of a compensatory hyperactivation of altered cognitive cerebellar connections. Finally, although the CCAS scale has proven able to detect CI in MS patients, its specificity for cerebellar pathology needs to be further investigated

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

Caracterização petrofísica das coquinas da Formação Morro do Chaves (Bacia de Sergipe-Alagoas) utilizando a tomografia computadorizada de raios X

Carbonate rocks constitute a large number of petroleum reservoirs worldwide. Notwithstanding, the characterization of these rocks is still a challenge due to their high complexity and pore space variability, indicating the importance of further studies to reduce uncertainty in reservoir interpretation and characterization. This work was performed for coquina samples from Morro do Chaves Formation (Sergipe-Alagoas Basin), analogous to important Brazilian reservoirs. Computed tomography (CT) was used for three-dimensional characterization of rock structure. The neural network named Self-Organizing Maps (SOM) was used for CT images segmentation. According to our tests, CT demonstrated to be a consistent tool for quantitative and qualitative analysis of heterogeneous pore space, by the evaluation of porosity, connectivity and the representative elementary volume.As rochas carbonáticas constituem um grande número de reservatórios de petróleo no mundo, contudo a caracterização dessas rochas ainda é um desafio em virtude de sua alta complexidade e da variabilidade do espaço poroso, indicando a im-portância de novos estudos para reduzir a incerteza associada à interpretação e caracterização dos reservatórios carbonáti-cos. Este trabalho foi realizado para amostras de coquinas da Formação Morro do Chaves — Bacia de Sergipe-Alagoas —, rochas análogas a importantes reservatórios brasileiros. A tomografia computadorizada (TC) de raios X foi empregada para a caracterização tridimensional da estrutura da rocha. A rede neural Self-Organizing Maps (SOM) foi utilizada para a seg-mentação das imagens tomográficas. De acordo com nossos testes, a TC demonstrou ser uma ferramenta consistente para a análise qualitativa e quantitativa de espaços porosos heterogêneos, avaliando a porosidade, a conectividade e o volume elementar representativo

Caracterização petrofísica das coquinas da formação Morro do Chaves (Bacia de Sergipe-Alagoas) utilizando a tomografia computadorizada de raios X

sem InformaçãoCarbonate rocks constitute a large number of petroleum reservoirs worldwide. Notwithstanding, the characterization of these rocks is still a challenge due to their high complexity and pore space variability, indicating the importance of further studies to183313sem Informaçãosem Informaçãosem Informaçã

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