Desenvolvimento de um phantom para controle de qualidade de imagens do tensor de difusão

Orientadores: Gabriela Castellano, Eduardo Tavares CostaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Imagens de ressonância magnética ponderadas por difusão (imagens DW) e imagens do tensor de difusão (imagens DT) combinam técnicas de ressonância magnética (MRI) e medidas de difusão. Nas imagens DW, o contraste é definido pelo movimento microscópico dos prótons de água. Atualmente, imagens DW tornaram-se populares em função de sua capacidade de detectar o AVC ainda na fase aguda. As imagens DT são calculadas a partir de imagens DW adquiridas em pelo menos seis direções, fornecendo informações de direcionalidade de difusão e tornando assim possível a reconstrução de imagens de fibras axonais ou musculares. Ambas as técnicas têm sido aplicadas ao estudo das estruturas corporais em condições saudáveis e patológicas. Atualmente, sabe-se que essas imagens e seus parâmetros são bastante sujeitos a fatores relacionados à aquisição e processamento. Heterogeneidades do campo magnético, suscetibilidade, deslocamento químico, interferência de radiofrequência (RF), correntes parasitárias e a razão sinal/ruído (SNR) têm efeitos mais nocivos em imagens DW do que em imagens ponderadas em T1 ou T2. No entanto, ainda hoje não existem phantoms ou protocolos padrão para controle de qualidade (QC) de imagens DW ou DT. Assim, o objetivo deste trabalho foi construir um phantom multipropósito, de baixo custo, para o QC de imagens DT. Diferentes linhas de pesca e configurações foram testadas para definir o tipo de fibras a ser usado. Em seguida, um phantom multipropósito foi desenvolvido e testado. Descobrimos que as linhas de pesca Dyneema® são a melhor opção para a construção do phantom. Os feixes obtidos a partir destas linhas têm valores de FA semelhantes aos encontrados nos tratos cerebrais principais e além disso não atrapalham outras medições de QC em MRI. O phantom multipropósito desenvolvido mostrou-se estável e pode ser usado para QC na rotina clínicaAbstract: Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) combine magnetic resonance imaging (MRI) techniques and diffusion measures. In DWI, the contrast is defined by the microscopic motion of water protons. Nowadays, DWI has become important for early diagnose of acute stroke. DT images are calculated from DW images acquired in at least six directions, which give information of diffusion directionality, making it possible to reconstruct axonal or muscle fiber images. Both techniques have been applied to study body structures in healthy and pathological conditions. Currently, it is known that these images and derived parameters are quite sensitive to factors related to acquisition and processing. Magnetic field non-homogeneity, susceptibility, chemical shift, radiofrequency (RF) interference, eddy currents and low signal-to-noise ratio (SNR) can have a more harmful effect in diffusion data than in T1- or T2-weighted image data. However, even today there are no standard phantoms or protocols for DWI or DTI quality control (QC). The aim of this work was to build a low-cost multipurpose DTI phantom to evaluate parameters that can compromise the quality of DTI data. Different fishing lines and setups were tested to define the kind of fibers to be used. Subsequently, a multipurpose phantom was developed and tested. We found that Dyneema® fishing lines are the best option to build the phantom. Dyneema® fishing lines bundles have FA values similar to those found in the main brain tracts and do not compromise other MRI QC measurements. Our tests showed that the multipurpose phantom built is stable and can be used for QC in clinical routineDoutoradoEngenharia BiomedicaDoutor em Engenharia ElétricaCNPQFAPES

Diffusion weighted magnetic resonance imaging : validation, correction and applications

Diffusion weighted Magnetic Resonance Imaging (DW-MRI) images the structure of the white substance in the brains and connections between various brain parts. To validate this technique a test object (phantom) was developed, the structure of which resembles the structure of the white substance. This phantom was brought on the market and by now over 15 universities and hospitals own one. Furthermore, DW-MRI was applied to a patient with Landau-Kleffner syndrome. This syndrome involves losing the ability of using and understanding language as a consequence of epilepsy. Intensive therapy can restore the language ability. Important connections for language processing look different in these patients and the brains use more visual information in communication. The dissertation also describes further studies with a blindsight patient. In these patients the eyes still function, but the visual information is no longer consciously processed. And yet, the deep brain parts that unconsciously process emotions and visual information appear active. The necessary brain connections were shown in this patient and they were not present in control persons. Therefore, the patient has created new connections

Assessing Optic Neuritis in a Mouse Model of Multiple Sclerosis with Diffusion MR Imaging

Optic neuritis (ON) is an early manifestation in patients of multiple sclerosis (MS), typically resulting in visual dysfunction. The inflammatory demyelination of the optic nerve in ON closely resembles pathologies of the rest of central nervous system (CNS) white matter in MS. Since accumulated axonal degeneration in MS was considered as the potential cause leading to permanent disability, correlating optic nerve pathology and visual function in ON could be a model system to investigate the relationship between functional outcome and neuropathology. It may also present a new way to reflect the disease progression in MS. Various MR techniques have been used to assess inflammation (inflammatory cell infiltration and vasogenic edema) of ON, but rarely demonstrated the ability to image cellularity changes non-invasively. Diffusion MRI measures the Brownian motion of water molecules in the microstructure of biological tissues. Diffusion tensor imaging (DTI) holds the promise to provide a specific biomarker of axonal injury and demyelination in CNS white matter by axial diffusivity (the diffusion parallel to white matter fibers) and radial diffusivity (the diffusion perpendicular to white matter fibers), respectively. However, DTI assumes a single diffusion tensor model and thus takes an average of varied diffusion components. In contrast, our recently developed diffusion basis spectrum imaging (DBSI) resolves the complex diffusion components and provides relatively accurate directional diffusivities and diffusion component fractions, relating to the detail and accurate pathological picture of the disease or injury. In the current work, in vivo 25-direction DBSI was applied to the optic nerve of mice with experimental autoimmune encephalomyelitis (EAE), an animal model of MS, with visual impairment at onset of ON. Our results demonstrate that inflammation correlated well with visual impairment in acute ON. DBSI successfully detected inflammatory cell infiltration and optic nerve white matter pathology in EAE that was consistent with histology, supporting the capability of DBSI to quantify increased cellularity, axonal injury and myelin damage in the optic nerve of EAE mice

Practical estimate of gradient nonlinearity for implementation of apparent diffusion coefficient bias correction

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109656/1/jmri24486.pd

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Autism spectrum disorders (ASDs) denote a significant growing public health concern. Currently, one in 68 children has been diagnosed with ASDs in the United States, and most children are diagnosed after the age of four, despite the fact that ASDs can be identified as early as age two. The ultimate goal of this thesis is to develop a computer-aided diagnosis (CAD) system for the accurate and early diagnosis of ASDs using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using first- and second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 25 infants with a high risk of developing ASDs. The preliminary diagnostic results are promising in identifying autistic from control patients

Improved Quantification of Connectivity in Human Brain Mapping

Diffusion magnetic resonance imaging (dMRI) is an advanced MRI methodology that can be used to probe the microstructure of biological tissue. dMRI can provide orientation information by modeling the process of water diffusion in white matter. This thesis presents contributions in three areas of diffusion imaging technology: diffusion reconstruction, quantification, and validation of derived metrics. It presents a novel reconstruction method by combining generalized q-sampling imaging, spherical harmonic basis functions and constrained spherical deconvolution methods to estimate the fiber orientation distribution function (ODF). This method provides improved spatial localization of brain nuclei and fiber tract separation. A novel diffusion anisotropy metric is presented that provides anatomically interpretable measurements of tracts that are robust in crossing areas of the brain. The metric, directional Axonal Volume (dAV) provides an estimate of directional water content of the tract based on the (ODF) and proton density map. dAV is a directionally sensitive metric and can separate anisotropic water content for each fiber population, providing a quantification in milliliters of water. A method is provided to map voxel-based dAV onto tracts that is not confounded by crossing areas and follows the tract morphology. This work introduces a novel textile based hollow fiber anisotropic phantom (TABIP) for validation of reconstruction and quantification methods. This provides a ground truth reference for axonal scale water tubular structures arranged in various anatomical configurations, crossing and mixing patterns. Analysis shows that: 1) the textile tracts are identifiable with scans used in human imaging and produced tracts and voxel metrics in the range of human tissue; 2) the current methods could resolve crossing at 90o and 45o but not 30o; 3) dAV/NODDI model closely matches (r=0.95) the number of fibers whereas conventional metrics poorly match (i.e., FA r=0.32). This work represents a new accurate quantification of axonal water content through diffusion imaging. dAV shows promise as a new anatomically interpretable metric of axonal connectivity that is not confounded by factors such as axon dispersion, crossing and local isotropic water content. This will provide better anatomical mapping of white matter and potentially improve the detection of axonal tract pathology

Integrated navigation and visualisation for skull base surgery

Skull base surgery involves the management of tumours located on the underside of the brain and the base of the skull. Skull base tumours are intricately associated with several critical neurovascular structures making surgery challenging and high risk. Vestibular schwannoma (VS) is a benign nerve sheath tumour arising from one of the vestibular nerves and is the commonest pathology encountered in skull base surgery. The goal of modern VS surgery is maximal tumour removal whilst preserving neurological function and maintaining quality of life but despite advanced neurosurgical techniques, facial nerve paralysis remains a potentially devastating complication of this surgery. This thesis describes the development and integration of various advanced navigation and visualisation techniques to increase the precision and accuracy of skull base surgery. A novel Diffusion Magnetic Resonance Imaging (dMRI) acquisition and processing protocol for imaging the facial nerve in patients with VS was developed to improve delineation of facial nerve preoperatively. An automated Artificial Intelligence (AI)-based framework was developed to segment VS from MRI scans. A user-friendly navigation system capable of integrating dMRI and tractography of the facial nerve, 3D tumour segmentation and intraoperative 3D ultrasound was developed and validated using an anatomically-realistic acoustic phantom model of a head including the skull, brain and VS. The optical properties of five types of human brain tumour (meningioma, pituitary adenoma, schwannoma, low- and high-grade glioma) and nine different types of healthy brain tissue were examined across a wavelength spectrum of 400 nm to 800 nm in order to inform the development of an Intraoperative Hypserpectral Imaging (iHSI) system. Finally, functional and technical requirements of an iHSI were established and a prototype system was developed and tested in a first-in-patient study

Imaging Pain And Brain Plasticity: A Longitudinal Structural Imaging Study

Chronic musculoskeletal pain is a leading cause of disability worldwide yet the mechanisms of chronification and neural responses to effective treatment remain elusive. Non-invasive imaging techniques are useful for investigating brain alterations associated with health and disease. Thus the overall goal of this dissertation was to investigate the white (WM) and grey matter (GM) structural differences in patients with musculoskeletal pain before and after psychotherapeutic intervention: cognitive behavioral therapy (CBT). To aid in the interpretation of clinical findings, we used a novel porcine model of low back pain-like pathophysiology and developed a post-mortem, in situ, neuroimaging approach to facilitate translational investigation. The first objective of this dissertation (Chapter 2) was to identify structural brain alterations in chronic pain patients compared to healthy controls. To achieve this, we examined GM volume and diffusivity as well as WM metrics of complexity, density, and connectivity. Consistent with the literature, we observed robust differences in GM volume across a number of brain regions in chronic pain patients, however, findings of increased GM volume in several regions are in contrast to previous reports. We also identified WM changes, with pain patients exhibiting reduced WM density in tracts that project to descending pain modulatory regions as well as increased connectivity to default mode network structures, and bidirectional alterations in complexity. These findings may reflect network level dysfunction in patients with chronic pain. The second aim (Chapter 3) was to investigate reversibility or neuroplasticity of structural alterations in the chronic pain brain following CBT compared to an active control group. Longitudinal evaluation was carried out at baseline, following 11-week intervention, and a four-month follow-up. Similarly, we conducted structural brain assessments including GM morphometry and WM complexity and connectivity. We did not observe GM volumetric or WM connectivity changes, but we did discover differences in WM complexity after therapy and at follow-up visits. To facilitate mechanistic investigation of pain related brain changes, we used a novel porcine model of low back pain-like pathophysiology (Chapter 6). This model replicates hallmarks of chronic pain, such as soft tissue injury and movement alteration. We also developed a novel protocol to perform translational post-mortem, in situ, neuroimaging in our porcine model to reproduce WM and GM findings observed in humans, followed by a unique perfusion and immersion fixation protocol to enable histological assessment (Chapter 4). In conclusion, our clinical data suggest robust structural brain alterations in patients with chronic pain as compared to healthy individuals and in response to therapeutic intervention. However, the mechanism of these brain changes remains unknown. Therefore, we propose to use a porcine model of musculoskeletal pain with a novel neuroimaging protocol to promote mechanistic investigation and expand our interpretation of clinical findings

Effect of Gradient Vectors Scheme and Noise Correction on Fractional Anisotropy in Diffusion Tensor Imaging of the Peripheral Nervous System

Diffusion Tensor Imaging (DTI) is a method widely used in research and clinic, especially for imaging and connectivity analysis of the white brain matter. Despite the many possibilities offered by DTI, this method suffers from an inherently low signal-to-noise ratio (SNR), since both the long echo time and the diffusion gradients weaken the signal. The SNR is particularly low at high spatial resolution, e.g. in the DTI of nerves. A low SNR leads to systematic and statistical errors in parameters calculated from the DTI, e.g. fractional anisotropy (FA). A low SNR can be partially compensated by increasing the number of diffusion directions or using methods for a posteriori noise correction. The most robust method for anatomical structures with unknown orientation is to distribute the diffusion gradients evenly in space. However, if the preferred direction of the anatomical structure is known in advance, it may be advantageous to limit the diffusion gradients to a cone centered on the axis of the structure. The aim of this work was to develop a DTI method with high accuracy and reliability for application in peripheral nerves. Two methods to reduce image noise were investigated: (1) A newly developed scheme of diffusion gradient vectors (DGV), where the vectors are restricted to a cone with an aperture angle Theta around the axis of the nerve and (2) different methods for a posteriori noise correction. For this purpose, Monte Carlo simulations were performed based on realistic values for diffusivity, FA and noise obtained from clinical investigations and studies. Furthermore, the methods were tested in a specially designed phantom simulating diffusion in peripheral nerves (FA = 0.65). These investigations were performed on a 3 Tesla whole-body magnetic resonance (MR) scanner. To determine the accuracy and reliability of the DTI using the appropriate measurement or correction procedures, systematic deviations of FA from baseline and the statistical error of FA were measured. The newly developed DGV scheme with limited space coverage was compared with gradient schemes with uniform space coverage (Jones, Downhill Simplex Method (DSM), gradient scheme of the manufacturer) based on their condition number (CN). The study showed that with the newly developed DGV scheme FA can be measured with high accuracy when the angle Theta is at least 45° or 60°. The minimum Theta depends on the number of gradient directions and on FA. Basically, the higher the FA value and the greater the number of gradients, the better the accuracy of the DGV scheme. For N = 30, the DGV allowed an exact determination of FA for the entire FA range (0.4 - 0.8) investigated in this study, if Theta ≥45° was. It could be shown that when using the new DGV scheme, a slight inclination of the investigated structure (≤30°) does not affect the accuracy of FA. CN of the developed DGV-scheme was higher than CN of the Jones-scheme and the DSM-scheme for N = 6; for N≥10 CN of the new DSM-scheme was lower than that of the Jones-scheme. However, it is also not to be expected that a method that concentrates the gradient vectors on a limited segment of space is as insensitive to interference as schemes with uniform gradient distribution. Nevertheless, the CN of the new DGV method was in the same order of magnitude as that of the other methods. A comparison of the different a posteriori correction methods showed that the power image method is the most effective and robust method and compensates for both the systematic and statistical errors of FA. The efficiency of the power image method is independent of the number of diffusion gradients used. In addition, the method works reliably - regardless of the method used for the coil combination (square sum versus adaptive combination). In contrast, both correction factor methods used in this study were less efficient in terms of noise correction; furthermore, the correction efficiency depended on the coil combination method. In conclusion, a combination of the newly developed DGV scheme with the power image method for a posteriori correction allows DTI of peripheral nerves with high SNR, high accuracy and reliability of the calculated parameters (e.g. FA) without the need for additional acquisition time. So far, however, these newly developed and tested methods have not yet been applied in studies or clinical trials

The challenge of mapping the human connectome based on diffusion tractography

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations