Insight into the fundamental trade-offs of diffusion MRI from polarization-sensitive optical coherence tomography in ex vivo human brain

In the first study comparing high angular resolution diffusion MRI (dMRI) in the human brain to axonal orientation measurements from polarization-sensitive optical coherence tomography (PSOCT), we compare the accuracy of orientation estimates from various dMRI sampling schemes and reconstruction methods. We find that, if the reconstruction approach is chosen carefully, single-shell dMRI data can yield the same accuracy as multi-shell data, and only moderately lower accuracy than a full Cartesian-grid sampling scheme. Our results suggest that current dMRI reconstruction approaches do not benefit substantially from ultra-high b-values or from very large numbers of diffusion-encoding directions. We also show that accuracy remains stable across dMRI voxel sizes of 1 ​mm or smaller but degrades at 2 ​mm, particularly in areas of complex white-matter architecture. We also show that, as the spatial resolution is reduced, axonal configurations in a dMRI voxel can no longer be modeled as a small set of distinct axon populations, violating an assumption that is sometimes made by dMRI reconstruction techniques. Our findings have implications for in vivo studies and illustrate the value of PSOCT as a source of ground-truth measurements of white-matter organization that does not suffer from the distortions typical of histological techniques.Published versio

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

An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging

In this paper we describe a method for retrospective estimation and correction of eddy current (EC)-induced distortions and subject movement in diffusion imaging. In addition a susceptibility-induced field can be supplied and will be incorporated into the calculations in a way that accurately reflects that the two fields (susceptibility- and EC-induced) behave differently in the presence of subject movement. The method is based on registering the individual volumes to a model free prediction of what each volume should look like, thereby enabling its use on high b-value data where the contrast is vastly different in different volumes. In addition we show that the linear EC-model commonly used is insufficient for the data used in the present paper (high spatial and angular resolution data acquired with Stejskal–Tanner gradients on a 3 T Siemens Verio, a 3 T Siemens Connectome Skyra or a 7 T Siemens Magnetome scanner) and that a higher order model performs significantly better. The method is already in extensive practical use and is used by four major projects (the WU-UMinn HCP, the MGH HCP, the UK Biobank and the Whitehall studies) to correct for distortions and subject movement

Automatic selection of multiple response functions for generalized Richardson-Lucy spherical deconvolution of diffusion MRI data

Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas), Universidade de Lisboa, Faculdade de Ciências, 2021O processo de desenvolvimento do cérebro humano tem sido objeto de estudo desde há vários anos, levando a avanços significativos no que diz respeito à compreensão das suas diferentes fases e mecanismos. Visto que este desenvolvimento resulta de uma série de complexos processos dinâmicos e adaptativos, existe uma busca contínua de informação sobre a sua organização estrutural e funcional, bem como o seu processo de maturação. A ressonância magnética de difusão (dMRI) é uma técnica bastante completa no que diz respeito à análise do cérebro in vivo. Esta técnica é utilizada para realizar um mapeamento quantitativo, através da aplicação de modelos como o modelo de difusão tensorial (DTI). Estes modelos fornecem medidas que caracterizam o cérebro, tais como a anisotropia fraccional (FA) e difusividade média (MD), permitindo assim a quantificação de microestruturas e consequentemente a reconstrução de feixes de substância branca (WM) que ligam diferentes regiões cerebrais. Dadas as suas propriedades de difusão anisotrópica e a sua constituição fibrosa, as fibras de WM têm sido amplamente estudadas através da dMRI. Além disso, a tractografia tornou-se a abordagem padrão no que diz respeito à avaliação da conectividade cerebral usando dados de dMRI. Os métodos de desconvolução esférica (SD) estão entre os mais utilizados para quantificar a distribuição da orientação das fibras (FOD) a partir de dados dMRI do cérebro, sendo que a forma mais comum de o fazer é com desconvolução esférica limitada (CSD). A ideia original da CSD baseia-se no facto de podermos escolher uma função de resposta (RF) representativa de um determinado tecido presente no cérebro e aplicar a SD para resolver o problema de cruzamento de fibras que o modelo de DTI não consegue resolver. Uma vez que o cérebro possui uma complexa organização de tecidos, múltiplos tecidos devem ser considerados. Não é apropriado usar uma RF de WM em todo o cérebro, pois isso pode levar a reconstruções imprecisas da orientação das fibras e a um mau desempenho durante o processo de tractografia. Ao ter em conta múltiplos tecidos, as propriedades da substância cinzenta (GM) e do líquido céfalo-raquidiano (CSF) podem ser quantificadas, e os efeitos de volume parcial (PVE) podem ser reduzidos. Nos últimos anos, tem sido possível adquirir dados “multi-camada” mais complexos e de elevada resolução, mesmo em recém-nascidos, o que permitiu melhorar a técnica de CSD. Consequentemente, esta aquisição também vai melhorar a reconstrução da FOD no cérebro adulto, pois considera os PVE entre diferentes tipos de tecidos. No cérebro neonatal existem algumas diferenças, pois este é constituído por WM em diferentes fases de maturação, e a GM possui características diferentes em comparação com um cérebro adulto. A possibilidade de distinguir diferentes tipos de fibras apenas com base nas suas características microestruturais deve-se às diferenças presentes no cérebro enquanto este se encontra numa fase de desenvolvimento. Em cérebros adultos, é menos provável conseguir observar tais diferenças. Uma das melhores formas de compreender e estudar estes processos de desenvolvimento cerebral é através do estudo do cérebro de neonatais. Como seria de esperar, o cérebro de um recém-nascido não se encontra completamente maturado, sofrendo por isso diversas alterações até estar totalmente desenvolvido. Estas mudanças vão desde o aumento do tamanho do cérebro a alterações ao nível vascular, levando consequentemente a uma alteração dos processos de cognitivos. Em última análise, a aplicação de CSD a dados de “multi-camada” leva a uma extração mais precisa da FOD que por sua vez irá melhorar o processo de tractografia e levará, consequentemente, a uma melhor compreensão do cérebro humano e do seu desenvolvimento, particularmente se aplicada em recém-nascidos e comparada com adultos. O método Generalized Richardson-Lucy (GRL) pode superar os problemas encontrados pela CSD através da realização de SD robusta, suprimindo picos imprecisos na FOD em dados “multi-camada” de dMRI. Este método pode definir múltiplos tecidos que irão aumentar a precisão da estimativa da FOD. No entanto, no método GRL, as três classes de tecidos representadas (WM, GM e CSF) são pré-definidas com valores FA e MD retirados da literatura. Este estudo consistiu em desenvolver um método que determina automaticamente o número de classes (tecidos) necessárias para aplicar corretamente GRL no cérebro com dados “multi-camada”, utilizando para isso os seus valores de FA e MD. O objetivo é aplicar corretamente o método de GRL no cérebro com as classes obtidas, de forma avaliar se existe uma melhoria no processo de estimação das FOD e por sua vez no processo de tractografia. Os dados utilizados neste trabalho consistem em dados de dMRI de dez neonatais e dez adultos, fornecidos pelo Developing Human Connectome Project (dHCP) e pelo Human Connectome Project (HCP), respetivamente. Estes dados já se encontravam num formato pré-processado, pelo que não foi necessário realizar qualquer etapa adicional neste sentido. A primeira parte do estudo consistiu no desenvolvimento do método de deteção automática do número de tipos de tecidos no cérebro. Para isso, todos os dados foram processados no ExploreDTI, um programa de interface gráfica para dados de dMRI e que permite, por exemplo, a realização de tractografia. Este programa foi também usado para extrair os valores de FA e MD dos dados de dMRI dos cérebros dos neonatais e dos adultos, de modo a analisar a sua distribuição de valores por todo o cérebro através de histogramas. De seguida foi aplicado um gaussian mixture model (GMM) aos histogramas de FA e MD, utilizando o MATLAB R2018a, de forma a decompor os dados em classes. Depois de aplicar o GMM aos dados, foi determinado o número ideal de Gaussianas para os mapas de FA e MD. Para isso foi calculado o Bayesian information criterion (BIC) de cada modelo, em que cada um destes se caracteriza por um certo número de Gaussianas. De seguida, foi calculada a probabilidade do valor de cada voxel pertencer a uma das classes escolhidas de FA e MD, atribuiu-se assim uma classe a cada voxel. Posteriormente selecionaram-se as três melhores combinações de FA e MD de cada classe com base na frequência de ocorrência de cada combinação, sendo que cada classe foi definida pela média e desvio padrão das respetivas Gaussianas. Por fim, foram criados mapas espaciais do cérebro com as classes finais, utilizando o MATLAB R2018a. Na segunda parte do estudo aplicou-se o método GRL aos dados, de forma a estimar a RF de cada um dos tecidos que foram selecionados na primeira parte. Estas duas partes do trabalho integram a nossa abordagem, sendo esta designada por "GRL-auto". No método GRL, a RF da GM e do CSF é baseada em valores de FA e MD retirados da literatura, enquanto que o método GRL-auto desenvolvido neste estudo estima esses valores através da seleção automática dos valores de FA e MD que são característicos de cada um destes tecidos. Obtiveram-se os mapas das frações de sinal da WM, GM, e CSF e foram feitas comparações entre o método GRL e GRL-auto. As FOD da WM obtidas com ambos os métodos foram comparadas entre si em regiões de cruzamento de fibras, tanto para neonatais como para os adultos. Por fim, para ambos os métodos, procedeu-se à tractografia em neonatais. Os resultados indicam que, tanto para recém-nascidos como para adultos, existe consistência em relação aos valores de FA e MD e ao seu respetivo número de classes selecionadas. Além disso, conseguem ser observadas diferentes fases de maturação de WM nos neonatais, mas também algumas imperfeições à volta dos ventrículos e regiões onde ocorre cruzamento de fibras. Todos os mapas espaciais de FA e MD fizeram sentido anatomicamente, sendo consistentes quer nos neonatais quer nos adultos, demonstrando assim a eficácia deste método. Os mapas de sinal das frações de WM, GM, e CSF apresentaram valores plausíveis e concordância com a anatomia esperada, para além de consistência tanto nos recém-nascidos como nos adultos. Os mapas de frações de sinal dos adultos praticamente não apresentaram diferenças entre os dois métodos. No entanto, os neonatais mostraram algumas diferenças notáveis, particularmente nos mapas de GM e CSF. Os resultados relativos às FODs não mostraram diferenças significativas no que diz respeito aos adultos. No entanto, para os neonatais, o método GRL-auto estimou FODs de elevada qualidade na WM, em comparação com o método GRL. Além disso, o método GRL-auto detetou mais picos plausíveis em regiões de cruzamento de fibras par além de uma diferença angular maior entre os principais picos das FOD, em comparação com o método GRL. Por fim, este método demonstrou uma melhoria no processo de tractografia, o que por sua vez levará a uma melhor compreensão do cérebro humano e do seu desenvolvimento. Conclui-se assim que o método desenvolvido neste estudo é eficiente e mostra consistência no que diz respeito ao processo de seleção automática do número de tecidos necessários para efetuar CSD no cérebro. Observou-se uma melhoria na tractografia das fibras, o que permitirá uma melhor compreensão da maturação do cérebro bem como das conexões entre as diversas regiões, tendo-se, assim, cumprido o objetivo principal deste trabalho.To understand the development of the human brain, more detailed information is required regarding the structural and functional cerebral organization and maturation. This development is the product of a complex series of dynamic and adaptive processes, and one of the best ways to understand it is through the study of the neonatal brain. The neonatal brain is not fully developed as it would be expected, so it goes through many changes regarding brain size, vasculature, and cognition. Constrained spherical deconvolution (CSD) is a widely used approach to quantify the fiber orientation distribution (FOD) from diffusion magnetic resonance imaging (dMRI) data of the brain, which allows the reconstruction of more complex white matter (WM) bundles in vivo, including in neonates. However, this method estimates the response function (RF) based on the model of a single fiber population and uses it to try to reconstruct the local WM orientations. Since the brain has a complex tissue organization, multiple tissues must be considered. It is not appropriate to use a WM RF throughout the whole brain because this can lead to spurious fiber orientation reconstructions and bad performance during fiber tractography. By accounting for multiple tissues, properties of grey matter (GM) and cerebrospinal fluid (CSF) can be captured, and partial volume effects (PVE) reduced. The acquisition of more comprehensive high-resolution multi-shell dMRI data offers opportunities to take into account multiple tissue types. Ultimately, these improve fiber tractography and consequently lead to a better understanding of the human brain and its development. The generalized Richardson-Lucy (GRL) method can overcome these challenges by performing robust spherical deconvolution (SD) and suppress spurious FOD peaks on multi-shell dMRI data due to PVE. However, in the GRL method, three tissue classes are typically pre-defined to represent WM, GM, and CSF, using fractional anisotropy (FA) and mean diffusivity (MD) values taken from literature. These two metrics are derived from the diffusion tensor model (DTI), with FA measuring how anisotropic is the tensor in each voxel and MD measuring the average of the diffusion rate at each voxel. This study aims to develop a method that automatically determines the number of tissue types (classes) that are needed to properly perform GRL in each analyzed brain dataset. The dataset used in this work consists of ten neonates and ten adults from the Developing Human Connectome Project (dHCP) and the Human Connectome Project (HCP), respectively. The first part of this study consisted of developing a method for the automatic detection of the number of tissue types in the brain, by applying a gaussian mixture model (GMM) and the Bayesian information criterion (BIC) to automatically extract the number of tissue classes from the histogram of dMRI properties. In the second part, the GRL method was applied to the data to estimate the RF of each tissue that was automatically chosen in the first part, and therefore calculate the FOD and perform fiber tractography. This approach was designated by “GRL-auto”. Lastly, a comparison between the basic GRL formulation and GRL-auto was done. Since GRL uses predefined values calibrated on HCP data, it becomes clear that small differences were expected on such dataset, whereas on dHCP larger differences were expected. Our analysis showed that our method automatically identified three classes in the FA histogram and two classes in the MD histogram when using HCP and dHCP data. Therefore, these results demonstrated consistency regarding the FA and MD values and their respective number of selected classes, for both datasets. Furthermore, different stages of WM maturation were detected in the dHCP data, but also some imperfections around the ventricles and crossing fibers areas. All FA and MD spatial maps were in line with anatomical correspondence and were consistent across all neonatal and adult subjects, demonstrating the efficiency of this method. The values of the WM, GM, and CSF fraction maps were plausible, in line with the expected anatomy, and looked consistent on both HCP and dHCP datasets. The signal fraction maps determined with the HCP data showed almost no difference between GRL and GRL-auto. However, in the dHCP data, we observed notable differences, particularly in the GM and CSF maps. Regarding the FOD estimation, our results showed no difference in the HCP data. Nevertheless, for the dHCP data, GRL-auto estimated high-quality FODs in WM, and detected more peaks in crossing fiber regions and a bigger angular difference between the main FOD peaks, as compared to GRL. Lastly, we showed that GRL-auto led to improvements in fiber tractography, which will likely support gaining a better understanding of the human brain and its development. Therefore, we can conclude that the method developed in this study is efficient and consistent in the automatic selection of the number of tissues needed to properly perform GRL in a brain, given multi-shell data, which was the main goal

Simulating rare events using a Weighted Ensemble-based string method

We introduce an extension to the Weighted Ensemble (WE) path sampling method to restrict sampling to a one dimensional path through a high dimensional phase space. Our method, which is based on the finite-temperature string method, permits efficient sampling of both equilibrium and non-equilibrium systems. Sampling obtained from the WE method guides the adaptive refinement of a Voronoi tessellation of order parameter space, whose generating points, upon convergence, coincide with the principle reaction pathway. We demonstrate the application of this method to several simple, two-dimensional models of driven Brownian motion and to the conformational change of the nitrogen regulatory protein C receiver domain using an elastic network model. The simplicity of the two-dimensional models allows us to directly compare the efficiency of the WE method to conventional brute force simulations and other path sampling algorithms, while the example of protein conformational change demonstrates how the method can be used to efficiently study transitions in the space of many collective variables

Investigating microstructural variation in the human hippocampus using non-negative matrix factorization

In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses

Diverging volumetric trajectories following pediatric traumatic brain injury.

Traumatic brain injury (TBI) is a significant public health concern, and can be especially disruptive in children, derailing on-going neuronal maturation in periods critical for cognitive development. There is considerable heterogeneity in post-injury outcomes, only partially explained by injury severity. Understanding the time course of recovery, and what factors may delay or promote recovery, will aid clinicians in decision-making and provide avenues for future mechanism-based therapeutics. We examined regional changes in brain volume in a pediatric/adolescent moderate-severe TBI (msTBI) cohort, assessed at two time points. Children were first assessed 2-5 months post-injury, and again 12 months later. We used tensor-based morphometry (TBM) to localize longitudinal volume expansion and reduction. We studied 21 msTBI patients (5 F, 8-18 years old) and 26 well-matched healthy control children, also assessed twice over the same interval. In a prior paper, we identified a subgroup of msTBI patients, based on interhemispheric transfer time (IHTT), with significant structural disruption of the white matter (WM) at 2-5 months post injury. We investigated how this subgroup (TBI-slow, N = 11) differed in longitudinal regional volume changes from msTBI patients (TBI-normal, N = 10) with normal WM structure and function. The TBI-slow group had longitudinal decreases in brain volume in several WM clusters, including the corpus callosum and hypothalamus, while the TBI-normal group showed increased volume in WM areas. Our results show prolonged atrophy of the WM over the first 18 months post-injury in the TBI-slow group. The TBI-normal group shows a different pattern that could indicate a return to a healthy trajectory

Tool for 3D analysis and segmentation of retinal layers in volumetric SD-OCT images

With the development of optical coherence tomography in the spectral domain (SD-OCT), it is now possible to quickly acquire large volumes of images. Typically analyzed by a specialist, the processing of the images is quite slow, consisting on the manual marking of features of interest in the retina, including the determination of the position and thickness of its different layers. This process is not consistent, the results are dependent on the clinician perception and do not take advantage of the technology, since the volumetric information that it currently provides is ignored. Therefore is of medical and technological interest to make a three-dimensional and automatic processing of images resulting from OCT technology. Only then we will be able to collect all the information that these images can give us and thus improve the diagnosis and early detection of eye pathologies. In addition to the 3D analysis, it is also important to develop visualization tools for the 3D data. This thesis proposes to apply 3D graphical processing methods to SD-OCT retinal images, in order to segment retinal layers. Also, to analyze the 3D retinal images and the segmentation results, a visualization interface that allows displaying images in 3D and from different perspectives is proposed. The work was based on the use of the Medical Imaging Interaction Toolkit (MITK), which includes other open-source toolkits. For this study a public database of SD-OCT retinal images will be used, containing about 360 volumetric images of healthy and pathological subjects. The software prototype allows the user to interact with the images, apply 3D filters for segmentation and noise reduction and render the volume. The detection of three surfaces of the retina is achieved through intensity-based edge detection methods with a mean error in the overall retina thickness of 3.72 0.3 pixels

Trade-off between angular and spatial resolutions in in vivo fiber tractography

Tractography is becoming an increasingly popular method to reconstruct white matter connections in vivo. The diffusion MRI data that tractography is based on requires a high angular resolution to resolve crossing fibers whereas high spatial resolution is required to distinguish kissing from crossing fibers. However, scan time increases with increasing spatial and angular resolutions, which can become infeasible in clinical settings. Here we investigated the trade-off between spatial and angular resolutions to determine which of these factors is most worth investing scan time in. We created a unique diffusion MRI dataset with 1.0mm isotropic resolution and a high angular resolution (100 directions) using an advanced 3D diffusion-weighted multi-slab EPI acquisition. This dataset was reconstructed to create subsets of lower angular (75, 50, and 25 directions) and lower spatial (1.5, 2.0, and 2.5mm) resolution. Using all subsets, we investigated the effects of angular and spatial resolutions in three fiber bundles-the corticospinal tract, arcuate fasciculus and corpus callosum-by analyzing the volumetric bundle overlap and anatomical correspondence between tracts. Our results indicate that the subsets of 25 and 50 directions provided inferior tract reconstructions compared with the datasets with 75 and 100 directions. Datasets with spatial resolutions of 1.0, 1.5, and 2.0mm were comparable, while the lowest resolution (2.5mm) datasets had discernible inferior quality. In conclusion, we found that angular resolution appeared to be more influential than spatial resolution in improving tractography results. Spatial resolutions higher than 2.0mm only appear to benefit multi-fiber tractography methods if this is not at the cost of decreased angular resolution