Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia), apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012; Nas últimas décadas, a esperança média de vida tem vindo a aumentar. Desta forma, é cada vez mais importante perceber como se pode providenciar um envelhecimento saudável. Apesar de ser comum relacionar o envelhecimento com a perda de capacidades cognitivas, estudos recentes sugerem que certas capacidades cognitivas podem ser preservadas, ou mesmo melhoradas, uma vez que o cérebro pode manter-se flexível ao longo dos anos. O projecto „Cambridge Centre for Ageing and Neuroscience‟ (Cam-CAN) é um projecto colaborativo que tem como objectivo determinar o grau de flexibilidade neural ao longo da vida e o potencial de reorganização neuronal para a sustentação das capacidades cognitivas. Esse conhecimento será importante para compreender como podemos preservar os recursos cognitivos e aumentar a qualidade de vida. Deste modo, pretende-se analisar dados demográficos, psicológicos, físicos e neuronais de 700 participantes com idades compreendidas entre os 18 e os 90 anos. Relativamente aos dados neuronais, este projecto inclui dados obtidos por magnetoencefalografia e por ressonância magnética estrutural...
Diffusion Kurtosis Imaging (DKI) is a fairly new magnetic resonance imag-ing (MRI) technique that tackles the non-gaussian motion of water in biological tissues by taking into account the restrictions imposed by tissue microstructure, which are not considered in Diffusion Tensor Imaging (DTI), where the water diffusion is considered purely gaussian. As a result DKI provides more accurate information on biological structures and is able to detect important abnormalities which are not visible in standard DTI analysis.
This work regards the development of a tool for DKI computation to be implemented as an OsiriX plugin. Thus, as OsiriX runs under Mac OS X, the pro-gram is written in Objective-C and also makes use of Apple’s Cocoa framework. The whole program is developed in the Xcode integrated development environ-ment (IDE).
The plugin implements a fast heuristic constrained linear least squares al-gorithm (CLLS-H) for estimating the diffusion and kurtosis tensors, and offers the user the possibility to choose which maps are to be generated for not only standard DTI quantities such as Mean Diffusion (MD), Radial Diffusion (RD), Axial Diffusion (AD) and Fractional Anisotropy (FA), but also DKI metrics, Mean Kurtosis (MK), Radial Kurtosis (RK) and Axial Kurtosis (AK).The plugin was subjected to both a qualitative and a semi-quantitative analysis which yielded convincing results. A more accurate validation pro-cess is still being developed...
The Orientation Distribution Function (ODF) is used to describe the directionality of multimodal diffusion in regions with complex fiber architecture present in brain and other biological tissues. In this paper, an approximation for the ODF of water diffusion from diffusional kurtosis imaging (DKI) is presented. DKI requires only a relatively limited number of diffusion measurements and, for the brain, b values no higher than 2500 s/mm2. The DKI-based ODF approximation is decomposed into two components representing the Gaussian and non-Gaussian (NG) diffusion contributions, respectively. Simulations of multiple fiber configurations show that both the total and the NG-ODF are able to resolve the orientations of the component fibers, with the NG-ODF being the most sensitive to profiling the fibers’ directions. Orientation maps obtained for in-vivo brain imaging data demonstrate multiple fiber components in brain regions with complex anatomy. The results appear to be in agreement with known white matter architecture.
Diffusional kurtosis imaging (DKI) is a clinically feasible extension of diffusion tensor imaging that probes restricted water diffusion in biological tissues using magnetic resonance imaging. Here we provide a physically meaningful interpretation of DKI metrics in white matter regions consisting of more or less parallel aligned fiber bundles by modeling the tissue as two non-exchanging compartments, the intra-axonal space and extra-axonal space. For the b-values typically used in DKI, the diffusion in each compartment is assumed to be anisotropic Gaussian and characterized by a diffusion tensor. The principal parameters of interest for the model include the intra- and extra-axonal diffusion tensors, the axonal water fraction and the tortuosity of the extra-axonal space. A key feature is that these can be determined directly from the diffusion metrics conventionally obtained with DKI. For three healthy young adults, the model parameters are estimated from the DKI metrics and shown to be consistent with literature values. In addition, as a partial validation of this DKI-based approach, we demonstrate good agreement between the DKI-derived axonal water fraction and the slow diffusion water fraction obtained from standard biexponential fitting to high b-value diffusion data. Combining the proposed WM model with DKI provides a convenient method for the clinical assessment of white matter in health and disease and could potentially provide important information on neurodegenerative disorders.
Diffusion kurtosis imaging (DKI) is a new diffusion magnetic resonance imaging (MRI) technique to go beyond the shortages of conventional diffusion tensor imaging (DTI) from the assumption that water diffuse in biological tissue is Gaussian. Kurtosis is used to measure the deviation of water diffusion from Gaussian model, which is called non-Gaussian, in DKI. However, the high-order kurtosis tensor in the model brings great difficulties in feature extraction. In this study, parameters like fractional anisotropy of kurtosis eigenvalues (FAek) and mean values of kurtosis eigenvalues (Mek) were proposed, and regional analysis was performed for 4 different tissues: corpus callosum, crossing fibers, thalamus, and cerebral cortex, compared with other parameters. Scatterplot analysis and Gaussian mixture decomposition of different parametric maps are used for tissues identification. Diffusion kurtosis information extracted from kurtosis tensor presented a more detailed classification of tissues actually as well as clinical significance, and the FAek of D-eigenvalues showed good sensitivity of tissues complexity which is important for further study of DKI.
Diffusion Kurtosis Imaging (DKI) provides quantifiable information on the non-Gaussian behavior of water diffusion in biological tissue. Changes in water diffusion tensor imaging (DTI) parameters and DKI parameters in several white and grey matter regions were investigated in a mild controlled cortical impact (CCI) injury rat model at both the acute (2 hours) and the sub-acute (7 days) stages following injury. Mixed model ANOVA analysis revealed significant changes in temporal patterns of both DTI and DKI parameters in the cortex, hippocampus, external capsule and corpus callosum. Post-hoc tests indicated acute changes in mean diffusivity (MD) in the bilateral cortex and hippocampus (p < 0.0005) and fractional anisotropy (FA) in ipsilateral cortex (p < 0.0005), hippocampus (p = 0.014), corpus callosum (p = 0.031) and contralateral external capsule (p = 0.011). These changes returned to baseline by the sub-acute stage. However, mean kurtosis (MK) was significantly elevated at the sub-acute stages in all ipsilateral regions and scaled inversely with the distance from the impacted site (cortex and corpus callosum: p < 0.0005; external capsule: p = 0.003; hippocampus: p = 0.011). Further, at the sub-acute stage increased MK was also observed in the contralateral regions compared to baseline (cortex: p = 0.032; hippocampus: p = 0.039) while no change was observed with MD and FA. An increase in mean kurtosis was associated with increased reactive astrogliosis from immunohistochemistry analysis. Our results suggest that DKI is sensitive to microstructural changes associated with reactive astrogliosis which may be missed by standard DTI parameters alone. Monitoring changes in MK allows the investigation of molecular and morphological changes in vivo due to reactive astrogliosis and may complement information available from standard DTI parameters. To date the use of diffusion tensor imaging has been limited to study changes in white matter integrity following traumatic insults. Given the sensitivity of DKI to detect microstructural changes even in the gray matter in vivo...
Diffusional kurtosis imaging (DKI) is a new technique based on non-Gaussian water diffusion analysis. However, the original DKI protocol (six b values and 30 motion-probing gradient (MPG) directions) requires more than 10 min of scanning time, which is too long for daily clinical use. We aimed to find suitable b value, MPG direction, and diffusion time settings for faster DKI. Four normal healthy subjects participated in the study. All DKI data sets were acquired on a clinical 3T-MRI scanner (Philips Medical Systems) with use of three protocols of 0–7500 s/mm2b values, 6–32 MPG directions, and 23–80 ms diffusion time. There was a remarkable difference in the standard deviation (SD) of the mean DK values in the number of MPG directions. The mean DK values were significantly higher in the posterior limb of the internal capsule (p = 0.003, r = 0.924) and thalamus (p = 0.005, r = 0.903), whereas the mean DK values of the cerebrospinal fluid (CSF) (p = 0.001, r = −0.976) were significantly lower when we used a longer diffusion time. Our results indicate that the SD of the mean DK values was higher in 15 MPG directions than in 20 MPG directions and more. Because the mean DK values of the CSF were significantly lower when we used longer diffusion times...
We report a case of a patient who developed a cerebral infarction, which was assessed using a new and advanced diffusion technique: diffusional kurtosis (DK) imaging. The signal changes on DK images were different from those on apparent diffusion coefficient (ADC) maps, and they seem to be useful for the prediction of early-stage tissue infarction. Although diffusion-weighted imaging and its metric, the ADC, have been widely used in the evaluation of stroke, DK imaging will provide additional and useful information, including a more detailed evaluation of pathologic tissue changes. This information can be predictive of the prognosis.
Diffusion kurtosis imaging (DKI) is a new method of magnetic resonance imaging (MRI) that provides non-Gaussian information that is not available in conventional diffusion tensor imaging (DTI). DKI requires data acquisition at multiple b-values for parameter estimation; this process is usually time-consuming. Therefore, fewer b-values are preferable to expedite acquisition. In this study, we carefully evaluated various acquisition schemas using different numbers and combinations of b-values. Acquisition schemas that sampled b-values that were distributed to two ends were optimized. Compared to conventional schemas using equally spaced b-values (ESB), optimized schemas require fewer b-values to minimize fitting errors in parameter estimation and may thus significantly reduce scanning time. Following a ranked list of optimized schemas resulted from the evaluation, we recommend the 3b schema based on its estimation accuracy and time efficiency, which needs data from only 3 b-values at 0, around 800 and around 2600 s/mm2, respectively. Analyses using voxel-based analysis (VBA) and region-of-interest (ROI) analysis with human DKI datasets support the use of the optimized 3b (0, 1000, 2500 s/mm2) DKI schema in practical clinical applications.
Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate...
The pathophysiology of major depressive disorder (MDD) and other stress related disorders has been associated with aberrations in the hippocampus and the frontal brain areas. More recently, other brain regions, such as the caudate nucleus, the putamen and the amygdala have also been suggested to play a role in the development of mood disorders. By exposing rats to a variety of stressors over a period of eight weeks, different phenotypes, i.e. stress susceptible (anhedonic-like) and stress resilient animals, can be discriminated based on the sucrose consumption test. The anhedonic-like animals are a well validated model for MDD. Previously, we reported that in vivo diffusion kurtosis imaging (DKI) of the hippocampus shows altered diffusion properties in chronically stressed rats independent of the hedonic state and that the shape of the right hippocampus is differing among the three groups, including unchallenged controls. In this study we evaluated diffusion properties in the prefrontal cortex, caudate putamen (CPu) and amygdala of anhedonic-like and resilient phenotypes and found that mean kurtosis in the CPu was significantly different between the anhedonic-like and resilient animals. In addition, axial diffusion and radial diffusion were increased in the stressed animal groups in the CPu and the amygdala...
Diffusion Kurtosis Imaging (DKI) is more sensitive to microstructural differences and can be related to more specific micro-scale metrics (e.g., intra-axonal volume fraction) than diffusion tensor imaging (DTI), offering exceptional potential for clinical diagnosis and research into the white and gray matter. Currently DKI is acquired only at low spatial resolution (2–3 mm isotropic), because of the lower signal-to-noise ratio (SNR) and higher artifact level associated with the technically more demanding DKI. Higher spatial resolution of about 1 mm is required for the characterization of fine white matter pathways or cortical microstructure. We used restricted-field-of-view (rFoV) imaging in combination with advanced post-processing methods to enable unprecedented high-quality, high-resolution DKI (1.2 mm isotropic) on a clinical 3T scanner. Post-processing was advanced by developing a novel method for Retrospective Eddy current and Motion ArtifacT Correction in High-resolution, multi-shell diffusion data (REMATCH). Furthermore, we applied a powerful edge preserving denoising method, denoted as multi-shell orientation-position-adaptive smoothing (msPOAS). We demonstrated the feasibility of high-quality, high-resolution DKI and its potential for delineating highly myelinated fiber pathways in the motor cortex. REMATCH performs robustly even at the low SNR level of high-resolution DKI...
Diffusion kurtosis imaging (DKI) is an extension of diffusion tensor imaging (DTI), exhibiting improved sensitivity and specificity in detecting developmental and pathological changes in neural tissues. However, little attention was paid to the performances of DKI and DTI in detecting white matter abnormality in schizophrenia. In this study, DKI and DTI were performed in 94 schizophrenia patients and 91 sex- and age-matched healthy controls. White matter integrity was assessed by fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK) of DKI and FA, MD, AD and RD of DTI. Group differences in these parameters were compared using tract-based spatial statistics (TBSS) (P < 0.01, corrected). The sensitivities in detecting white matter abnormality in schizophrenia were MK (34%) > AK (20%) > RK (3%) and RD (37%) > FA (24%) > MD (21%) for DKI, and RD (43%) > FA (30%) > MD (21%) for DTI. DKI-derived diffusion parameters (RD, FA and MD) were sensitive to detect abnormality in white matter regions (the corpus callosum and anterior limb of internal capsule) with coherent fiber arrangement; however, the kurtosis parameters (MK and AK) were sensitive to reveal abnormality in white matter regions (the juxtacortical white matter and corona radiata) with complex fiber arrangement. In schizophrenia...
Image denoising has a profound impact on the precision of estimated parameters in diffusion kurtosis imaging (DKI). This work first proposes an approach to constructing a DKI phantom that can be used to evaluate the performance of denoising algorithms in regard to their abilities of improving the reliability of DKI parameter estimation. The phantom was constructed from a real DKI dataset of a human brain, and the pipeline used to construct the phantom consists of diffusion-weighted (DW) image filtering, diffusion and kurtosis tensor regularization, and DW image reconstruction. The phantom preserves the image structure while minimizing image noise, and thus can be used as ground truth in the evaluation. Second, we used the phantom to evaluate three representative algorithms of non-local means (NLM). Results showed that one scheme of vector-based NLM, which uses DWI data with redundant information acquired at different b-values, produced the most reliable estimation of DKI parameters in terms of Mean Square Error (MSE), Bias and standard deviation (Std). The result of the comparison based on the phantom was consistent with those based on real datasets.
Explosive blast-related injuries are one of the hallmark injuries of veterans returning from recent wars, but the effects of a blast overpressure on the brain are poorly understood. In this study, we used in vivo diffusion kurtosis imaging (DKI) and proton magnetic resonance spectroscopy (MRS) to investigate tissue microstructure and metabolic changes in a novel, direct cranial blast traumatic brain injury (dc-bTBI) rat model. Imaging was performed on rats before injury and 1, 7, 14 and 28 days after blast exposure (~517 kPa peak overpressure to the dorsum of the head). No brain parenchyma abnormalities were visible on conventional T2-weighted MRI, but microstructural and metabolic changes were observed with DKI and proton MRS, respectively. Increased mean kurtosis, which peaked at 21 days post injury, was observed in the hippocampus and the internal capsule. Concomitant increases in myo-Inositol (Ins) and Taurine (Tau) were also observed in the hippocampus, while early changes at 1 day in the Glutamine (Gln) were observed in the internal capsule, all indicating glial abnormality in these regions. Neurofunctional testing on a separate but similarly treated group of rats showed early disturbances in vestibulomotor functions (days 1–14)...
Diffusion-weighted magnetic resonance imaging (DW-MRI) is considered part of the standard imaging protocol for the evaluation of patients with prostate cancer. It has been proven valuable as a functional tool for qualitative and quantitative analysis of prostate cancer beyond anatomical MRI sequences such as T2-weighted imaging. This review discusses ongoing controversies in DW-MRI acquisition, including the optimal number of b-values to be used for prostate DWI, and summarizes the current literature on the use of advanced DW-MRI techniques. These include intravoxel incoherent motion imaging, which better accounts for the non-mono-exponential behavior of the apparent diffusion coefficient as a function of b-value and the influence of perfusion at low b-values. Another technique is diffusion kurtosis imaging (DKI). Metrics from DKI reflect excess kurtosis of tissues, representing its deviation from Gaussian diffusion behavior. Preliminary results suggest that DKI findings may have more value than findings from conventional DW-MRI for the assessment of prostate cancer.
Mestrado em Radiações Aplicadas às Tecnologias da Saúde. Área de especialização: Ressonância Magnética; Objectivo: Analisar quantitativamente a Difusão por RM da mama, através do Coeficiente de Difusão Aparente (ADC) em Imagem ponderada em Difusão (DWI) e Kurtosis Média (MK) em Imagem por Kurtosis de Difusão (DKI), de alguns tipos de lesões mamárias.
Metodologia: Este estudo foi realizado com a autorização da Comissão de Ética e Comissão Científica do Instituto Português de Oncologia de Lisboa Francisco Gentil, E.P.E. e as utentes deram o seu consentimento informado quanto à participação no estudo. Consideraram-se 20 utentes do sexo feminino, com idade média±desvio padrão de 58,78±12,27anos (dos 40 a 83anos) e 23 casos de lesões mamárias: 3 benignas - 3 Fibroadenomas (FA); e 20 malignas - 16 Carcinomas Ductais Invasivos (CDI), 2 Carcinomas Ductais In Situ (CDIS), 2 Carcinomas Lobulares Invasivos (ILC). Utilizou-se um equipamento de RM de 1,5T com uma bobina específica para a mama. Além do protocolo normal aplicou-se durante o exame uma sequência adicional de Difusão com 6 valores de b (0, 50, 250, 500, 750 e 1000s/mm2). Seleccionaram-se os melhores valores de b a utilizar na prática clínica, obteve-se o valor de ADC através do ajuste exponencial dos valores de intensidade de sinal das lesões estudadas (ADCajuste) e do valor de ADC produzido automaticamente pelo equipamento (mapa ADC). Obtiveram-se também os valores de difusividade média equivalente ao ADC no modelo não Gaussiano (MD) e MK para as mesmas lesões através de um ajuste não linear para um modelo de difusão não Gaussiana. Analisaram-se as diferenças entre as lesões estudadas quanto ao seu tipo e histologia de acordo com os parâmetros quantificadores de Difusão considerados.
Resultados: Os resultados indicam que a exclusão dos valores b=0s/mm2 reflecte ajustes com qualidade superior (R2≈1) em 60...
The ensemble average diffusion propagator (EAP) obtained from diffusion MRI (dMRI) data captures important structural properties of the underlying tissue. As such, it is imperative to derive an accurate estimate of the EAP from the acquired diffusion data. In this work, we propose a novel method for estimating the EAP by representing the diffusion signal as a linear combination of directional radial basis functions scattered in q-space. In particular, we focus on a special case of anisotropic Gaussian basis functions and derive analytical expressions for the diffusion orientation distribution function (ODF), the return-to-origin probability (RTOP), and mean-squared-displacement (MSD). A significant advantage of the proposed method is that the second and the fourth order moment tensors of the EAP can be computed explicitly. This allows for computing several novel scalar indices (from the moment tensors) such as mean-fourth-order-displacement (MFD) and generalized kurtosis (GK) – which is a generalization of the mean kurtosis measure used in diffusion kurtosis imaging. Additionally, we also propose novel scalar indices computed from the signal in q-space, called the q-space mean-squared-displacement (QMSD) and the q-space mean-fourth-order-displacement (QMFD)...
The displacement distribution of a water molecular is characterized
mathematically as Gaussianity without considering potential diffusion barriers
and compartments. However, this is not true in real scenario: most biological
tissues are comprised of cell membranes, various intracellular and
extracellular spaces, and of other compartments, where the water diffusion is
referred to have a non-Gaussian distribution. Diffusion kurtosis imaging (DKI),
recently considered to be one sensitive biomarker, is an extension of diffusion
tensor imaging, which quantifies the degree of non-Gaussianity of the
diffusion. This work proposes an efficient scheme of maximum likelihood
estimation (MLE) in DKI: we start from the Rician noise model of the signal
intensities. By augmenting a Von-Mises distributed latent phase variable, the
Rician likelihood is transformed to a tractable joint density without loss of
generality. A fast computational method, an expectation-maximization (EM)
algorithm for MLE is proposed in DKI. To guarantee the physical relevance of
the diffusion kurtosis we apply the ternary quartic (TQ) parametrization to
utilize its positivity, which imposes the upper bound to the kurtosis. A
Fisher-scoring method is used for achieving fast convergence of the individual
diffusion compartments. In addition...
Structural connectivity models based on Diffusion Tensor Imaging (DTI) are strongly
affected by the technique’s inability to resolve crossing fibres, either intra- or inter-hemispherical connections. Several models have been proposed to address this issue, including an algorithm aiming to resolve crossing fibres which is based on Diffusion Kurtosis Imaging (DKI). This technique is clinically feasible, even when multi-band acquisitions are not available, and compatible with multi-shell acquisition schemes. DKI is an extension of DTI enabling the estimation of diffusion tensor and diffusion kurtosis metrics. In this study we compare the performance of DKI and DTI in performing structural brain connectivity.
Six healthy subjects were recruited, aged between 25 and 35 (three females). The MRI
experiments were performed using a 3T Siemens Trio with a 32-channel head coil. The
scans included a T1-weighted sequence (1mm3), and a DWI with b-values 0, 1000 and