Deterministic diffusion fiber tracking improved by quantitative anisotropy

Diffusion MRI tractography has emerged as a useful and popular tool for mapping connections between brain regions. In this study, we examined the performance of quantitative anisotropy (QA) in facilitating deterministic fiber tracking. Two phantom studies were conducted. The first phantom study examined the susceptibility of fractional anisotropy (FA), generalized factional anisotropy (GFA), and QA to various partial volume effects. The second phantom study examined the spatial resolution of the FA-aided, GFA-aided, and QA-aided tractographies. An in vivo study was conducted to track the arcuate fasciculus, and two neurosurgeons blind to the acquisition and analysis settings were invited to identify false tracks. The performance of QA in assisting fiber tracking was compared with FA, GFA, and anatomical information from T 1-weighted images. Our first phantom study showed that QA is less sensitive to the partial volume effects of crossing fibers and free water, suggesting that it is a robust index. The second phantom study showed that the QA-aided tractography has better resolution than the FA-aided and GFA-aided tractography. Our in vivo study further showed that the QA-aided tractography outperforms the FA-aided, GFA-aided, and anatomy-aided tractographies. In the shell scheme (HARDI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 30.7%, 32.6%, and 24.45% of the false tracks, respectively, while the QA-aided tractography has 16.2%. In the grid scheme (DSI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 12.3%, 9.0%, and 10.93% of the false tracks, respectively, while the QA-aided tractography has 4.43%. The QA-aided deterministic fiber tracking may assist fiber tracking studies and facilitate the advancement of human connectomics. © 2013 Yeh et al

Impact of Gradient Number and Voxel Size on Diffusion Tensor Imaging Tractography for Resective Brain Surgery

OBJECTIVE: To explore quantitatively and qualitatively how the number of gradient directions (NGD) and spatial resolution (SR) affect diffusion tensor imaging (DTI) tractography in patients planned for brain tumor surgery, using routine clinical magnetic resonance imaging protocols. METHODS: Of 67 patients with intracerebral lesions who had 2 different DTI scans, 3 DTI series were reconstructed to compare the effects of NGD and SR. Tractographies for 4 clinically relevant tracts (corticospinal tract, superior longitudinal fasciculus, optic radiation, and inferior fronto-occipital fasciculus) were constructed with a probabilistic tracking algorithm and automated region of interest placement and compared for 3 quantitative measurements: tract volume, median fiber density, and mean fractional anisotropy, using linear mixed-effects models. The mean tractography volume and intersubject reliability were visually compared across scanning protocols, to assess the clinical relevance of the quantitative differences. RESULTS: Both NGD and SR significantly influenced tract volume, median fiber density, and mean fractional anisotropy, but not to the same extent. In particular, higher NGD increased tract volume and median fiber density. More importantly, these effects further increased when tracts were affected by disease. The effects were tract specific, but not dependent on threshold. The superior longitudinal fasciculus and inferior fronto-occipital fasciculus showed the most significant differences. Qualitative assessment showed larger tract volumes given a fixed confidence level, and better intersubject reliability for the higher NGD protocol. SR in the range we considered seemed less relevant than NGD. CONCLUSIONS: This study indicates that, under time constraints of clinical imaging, a higher number of diffusion gradients is more important than spatial resolution for superior DTI probabilistic tractography in patients undergoing brain tumor surgery

Spectral Clustering en IRM de diffusion pour Retrouver les Faisceaux de la Matière Blanche

White matter fiber clustering allows to get insight about anatomical structures in order to generate atlases, perform clear visualizations and compute statistics across subjects, all important and current neuroimaging problems. In this work, we present a Diffusion Maps clustering method applied to diffusion MRI in order to cluster and segment complex white matter fiber bundles. It is well-known that Diffusion Tensor Imaging (DTI) is restricted in complex fiber regions with crossings and this is why recent High Angular Resolution Diffusion Imaging (HARDI) such has Q-Ball Imaging (QBI) have been introduced to overcome these limitations. QBI reconstructs the diffusion orientation distribution function (ODF), a spherical function that has its maxima agreeing with the underlying fiber populations. In this paper, we introduce the usage of the Diffusion Maps technique and show how it can be used to directly cluster set of fiber tracts, that could be obtained through a streamline tractography for instance, and how it can also help in segmenting fields of ODF images, obtained through a linear and regularized ODF estimation algorithm based on a spherical harmonics representation of the Q-Ball data. We first show the advantage of using Diffusion Maps clustering over classical methods such as N-Cuts and Laplacian Eigenmaps in both cases. In particular, our Diffusion Maps requires a smaller number of hypothesis from the input data, reduces the number of artifacts in fiber tract clustering and ODF image segmentation and automatically exhibits the number of clusters in both cases by using an adaptive scale-space parameter. We also show that our ODF Diffusion Maps clustering can reproduce published results using the diffusion tensor (DT) clustering with N-Cuts on simple synthetic images without crossings. On more complex data with crossings, we show that our ODF-based method succeeds to separate fiber bundles and crossing regions whereas the DT-based methods generate artifacts and exhibit wrong number of clusters. Finally, we illustrate the potential of our approach on a real brain dataset where we successfully segment well-known fiber bundles

A comparison of methods for the registration of tractographic fibre images

Diffusion tensor imaging (DTI) and tractography have opened up new avenues in neuroscience. As most applications require precise spatial localization of the fibre images, image registration is an important area of research. Registration is usually performed prior to tractography. However more reliable images could be produced if a viable registration can be performed post tractography. This study shows two available techniques for direct registration of fibre images and explores novel adaptations of these. The methods register volume images derived from the fibres, and reapply the transformation from these registrations to the fibre images. The first method is a local affine registration and the second is a global affine registration. The local affine method produced superior results

Long-term micro-structure and cerebral blood flow changes in patients recovered from COVID-19 without neurological manifestations

The coronavirus disease 2019 (COVID-19) rapidly progressed to a global pandemic. Although patients totally recover from COVID-19 pneumonia, long-term effects on the brain still need to be explored. Here, two subtypes (mild type-MG and severe type-SG) with no specific neurological manifestations at the acute stage and no obvious lesions on the conventional MRI three months after discharge were recruited. Changes in gray matter morphometry, cerebral blood flow (CBF) and white matter (WM) microstructure were investigated using MRI. The relationship between brain imaging measurements and inflammation markers were further analyzed. Compared with healthy controls, the decrease in cortical thickness/CBF, and the changes in WM microstructure were observed to be more severe in the SG than MG, especially in the frontal and limbic systems. Furthermore, changes in brain microstructure, CBF and tracts parameters were significantly correlated with inflammatory markers. The indirect injury related to inflammatory storm may damage the brain, that led to these interesting observations. There are also other likely potential causes, such as hypoxemia and dysfunction of vascular endothelium, et al. The abnormalities in these brain areas need to be monitored in the process of complete recovery, which could help clinicians to understand the potential neurological sequelae of COVID-19. Key words: COVID-19; Brain MRI; Recovered patients; Inflammatory markers; Cortical thickness; Cerebral blood flow; microstructure; gray matter; white matter; subcortical nucle

Automatic atlas-based segmentation of brain white-matter in neonates at risk for neurodevelopmental disorders

Dissertação de Mestrado Integrado em Engenharia Biomédica apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra.Na Europa, mais de meio milhão de bebés nasce prematuramente por ano. Os recémnascidos com menos de 32 semanas de gestação estão especialmente em risco para desordens de desenvolvimento neuronal. Para estes bebés, os principais problemas de desenvolvimento surgem a nível cognitivo (40%). Reabilitação é possível, principalmente se for feita nos primeiros tempos de vida quando o cérebro é caracterizado pela sua enorme plasticidade. No entanto, não existem bio-marcadores que possibilitem prever quais os bebés prematuros que estão em risco. Este trabalho tem como objetivo analisar a maturação da matéria branca do cérebro em bebés prematuros e investigar a sua usabilidade como possível marcador para desordens de desenvolvimento neuronal. Um pipeline automático para segmentação atlas-based de matéria branca visualizada com tratografia de Diffusion Tensor Imaging (DTI) foi implementado. O atlas usado foi construído previamente com tratografias de bebés prematuros em term equivalente age (TEA). Principais contribuições correspondem à automatização do pipeline e desenvolvimento de algoritmos específicos para tratografias neonatais para: registo entre tratografias, skull-stripping e sampling. O algoritmo para registo entre tratografias foi inspirado no trabalho de O’Donnell (2012). Este tipo de registo utiliza a informação relativa à conectividade global de regiões de matéria branca no cérebro, característica dos dados de tratografia. Em comparação com métodos de segmentação manual, este método consome menos tempo e é menos dependente do utilizador. Os resultados são promissores, apenas 12% das segmentações contêm mais de 30% de fibras erroneamente segmentadas por estrutura anatómica. A performance da segmentação não foi influenciada pela presença de patologias da matéria branca nos pacientes. As estruturas anatómicas automaticamente segmentadas do corpus callosum foram também analisadas relativamente aos seus volumes e valores de anisotropia. Volume e difusão média são significamente correlacionados com a intensidade de patologia da matéria branca. Estes resultados estão de acordo com descobertas prévias sobre como patologia na matéria branca influencia os valores de anisotropia. Em conclusão, tratografia neonatal pode ser segmentada nas principais estruturas anatómicas de interesse para estudo de desordens do desenvolvimento neuronal

Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data

A probabilistic framework for registering generalised point sets comprising multiple voxel-wise data features such as positions, orientations and scalar-valued quantities, is proposed. It is employed for the analysis of magnetic resonance diffusion tensor image (DTI)-derived quantities, such as fractional anisotropy (FA) and fibre orientation, across multiple subjects. A hybrid Student’s t-Watson-Gaussian mixture model-based non-rigid registration framework is formulated for the joint registration and clustering of voxel-wise DTI-derived data, acquired from multiple subjects. The proposed approach jointly estimates the non-rigid transformations necessary to register an unbiased mean template (represented as a 7-dimensional hybrid point set comprising spatial positions, fibre orientations and FA values) to white matter regions of interest (ROIs), and approximates the joint distribution of voxel spatial positions, their associated principal diffusion axes, and FA. Specific white matter ROIs, namely, the corpus callosum and cingulum, are analysed across healthy control (HC) subjects (K = 20 samples) and patients diagnosed with mild cognitive impairment (MCI) (K = 20 samples) or Alzheimer’s disease (AD) (K = 20 samples) using the proposed framework, facilitating inter-group comparisons of FA and fibre orientations. Group-wise analyses of the latter is not afforded by conventional approaches such as tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM)

Automated multi-subject fiber clustering of mouse brain using dominant sets

Mapping of structural and functional connectivity may provide deeper understanding of brain function and disfunction. Diffusion Magnetic Resonance Imaging (DMRI) is a powerful technique to non-invasively delineate white matter (WM) tracts and to obtain a three-dimensional description of the structural architecture of the brain. However, DMRI tractography methods produce highly multi-dimensional datasets whose interpretation requires advanced analytical tools. Indeed, manual identification of specific neuroanatomical tracts based on prior anatomical knowledge is time-consuming and prone to operator-induced bias. Here we propose an automatic multi-subject fiber clustering method that enables retrieval of group-wise WM fiber bundles. In order to account for variance across subjects, we developed a multi-subject approach based on a method known as Dominant Sets algorithm, via an intra- and cross-subject clustering. The intra-subject step allows us to reduce the complexity of the raw tractography data, thus obtaining homogeneous neuroanatomically-plausible bundles in each diffusion space. The cross-subject step, characterized by a proper space-invariant metric in the original diffusion space, enables the identification of the same WM bundles across multiple subjects without any prior neuroanatomical knowledge. Quantitative analysis was conducted comparing our algorithm with spectral clustering and affinity propagation methods on synthetic dataset. We also performed qualitative analysis on mouse brain tractography retrieving significant WM structures. The approach serves the final goal of detecting WM bundles at a population level, thus paving the way to the study of the WM organization across groups.Mapping of structural and functional connectivity may provide deeper understanding of brain function and disfunction. Diffusion Magnetic Resonance Imaging (DMRI) is a powerful technique to non-invasively delineate white matter (WM) tracts and to obtain a three-dimensional description of the structural architecture of the brain. However, DMRI tractography methods produce highly multi-dimensional datasets whose interpretation requires advanced analytical tools. Indeed, manual identification of specific neuroanatomical tracts based on prior anatomical knowledge is time-consuming and prone to operator-induced bias. Here we propose an automatic multi-subject fiber clustering method that enables retrieval of group-wise WM fiber bundles. In order to account for variance across subjects, we developed a multi-subject approach based on a method known as Dominant Sets algorithm, via an intra-and cross-subject clustering. The intra-subject step allows us to reduce the complexity of the raw tractography data, thus obtaining homogeneous neuroanatomically-plausible bundles in each diffusion space. The cross-subject step, characterized by a proper space-invariant metric in the original diffusion space, enables the identification of the same WM bundles across multiple subjects without any prior neuroanatomical knowledge. Quantitative analysis was conducted comparing our algorithm with spectral clustering and affinity propagation methods on synthetic dataset. We also performed qualitative analysis on mouse brain tractography retrieving significant WM structures. The approach serves the final goal of detecting WM bundles at a population level, thus paving the way to the study of the WM organization across groups