Optimization of the diffusion-weighted MRI processing pipeline for the longitudinal assessment of the brain microstructure in a rat model of Alzheimer’s disease

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia) Universidade de Lisboa, Faculdade de Ciências, 2019The mechanism that triggers Alzheimer’s disease (AD) is not well-established, with amyloid plaques, neurofibrillary tangles of tau protein, microgliosis and glucose hypometabolism all likely involved in the early cascade. One main advantage of animal models is the possibility to tease out the impact of each insult on the neurodegeneration. Following an intracerebroventricular (icv) injection of streptozotocin (STZ), rats and monkeys develop impaired brain glucose metabolism, i.e. “diabetes of the brain”. Nu-merous studies have reported AD-like features in icv-STZ animals, but this model has never been char-acterized in terms of Magnetic Resonance Imaging (MRI)-derived biomarkers beyond structural brain atrophy. White matter degeneration has been proposed as a promising biomarker for AD that well pre-cedes cortical atrophy and correlates strongly with disease severity. Therefore, this project proposes a longitudinal study of white matter degeneration in icv-STZ rats using diffusion MRI. An existing image processing pipeline was primarily used to obtain preliminary results and propose an optimization strat-egy to improve it in terms of data quality and reliability. These strategies were tested and implemented in the pipeline when confirmed to be valuable, in order to achieve results as reproducible as possible and find the spatio-temporal pattern of brain degeneration in this animal model. All experiments were approved by the local Service for Veterinary Affairs. Male Wistar rats (N=18) (236±11 g) underwent a bilateral icv-injection of either streptozotocin (3 mg/kg, STZ group, N=10) or buffer (control group, CTL, N=8). Rats were scanned at four timepoints following surgery on a 14 T Varian system. Diffusion data were acquired using a semi-adiabatic SE-EPI PGSE sequence as follows: 4 (b=0 ms/μm2), 12 (b=0.8 ms/μm2), 16 (b=1.3 ms/μm2) and 30 (b=2 ms/μm2) directions; TE/TR=48/2500 ms, 9 coronal 1 mm slices, δ/Δ=4/27 ms, FOV=23x17 mm2, matrix=128x64 and 4 shots. The existing image processing pipeline included image denoising and eddy-correction. Moreover, diffusion and kurtosis tensors were calculated for each voxel, producing parametric maps of fractional anisotropy (FA), mean, axial and radial diffusivity (MD, AxD and RD) and mean, axial and radial kur-tosis (MK, AK and RK). Additionally, the two-compartment WMTI-Watson model was further esti-mated to provide specificity to the microstructure assessment. The following metrics were derived from the model: volume water fraction , parallel intra-axonal diffusivity , parallel ,║ and perpendicular extra-axonal diffusivities ,ꓕ and dispersion of fiber orientations 2. Since the model allows for two mathematical solutions, the >,║ solution was retained based on recent evidence. Considering pre-vious findings, the corpus callosum, cingulum, fornix and fimbria were chosen as white matter regions of interest (ROIs) and automatically segmented using anatomical atlas-based registration. Mean diffu-sion metrics were calculated in each ROI for each dataset. CTL and STZ groups were compared using two-sided t-tests at each timepoint. Within-group longitudinal changes were assessed using one-way ANOVA. Because of the small cohort, statistical analysis excluded the last time point. In the course of this project, strategies to optimize the existing pipeline were developed and tested. The existing brain atlas template was supplemented with white matter labels, rat brain extraction was semi-automated, and bias field correction of anatomical data was added before registration. Ventricle enlargement is typically reported in icv-STZ animals and normally constitutes an issue of misalignment in registration. In order to better match the label ROIs with the respective underlying tissue, several registration procedures were tested with different FA and color-coded FA template images. Color-coded FA-based registration dramatically improved the segmentation of the corpus callosum and the fimbria and reliability of diffusion metrics extracted from these regions. Moreover, additional fiber metrics were extracted from a newly developed tractography pipeline to compare with tensors metrics and finally, tensors metrics were evaluated in the gray matter for a more comprehensive spatio-temporal character-ization of brain degeneration. Results from statistical analysis were obtained after implementing the successful optimization strat-egies into the pipeline. There were few significant differences within groups over time. However, be-tween-group differences at each time point were more pronounced. White matter microstructure altera-tions were consistent with previous studies of histology and cognitive performance of the icv-STZ model. Changes in tensors metrics indicate early axonal injury in the fimbria and fornix at 2 weeks after injection, a period of potential recovery at 6 weeks after injection and late axonal injury at 13 weeks in all ROIs. The WMTI-Watson biophysical model provided specificity to the underlying microstructure, by showing intra-axonal damage in the fimbria and corpus callosum as early as 2 weeks, followed by a recover period and definite axonal loss at 13 weeks after injection. Results from tensors metrics and the WMTI-Watson model are not only complementary, they are consistent with each other and with previously-established trends for structural thickness, memory per-formance, amyloid deposition and inflammation. The icv-STZ model displays white matter changes in tracts reportedly affected by AD, while the degeneration is induced primarily by impaired brain glucose metabolism. The icv-STZ constitutes an excellent model to reproduce sporadic AD and should allow to further explore the hypothesis of AD being “type III diabetes”. The combination of diffusion information extracted from tensor imaging and biophysical modelling is a promising set of tools to assess white matter in the AD brain and might be the upcoming strategy to assess the human brain. Regarding future work, it will focus on estimating the correlation between microstructural alterations and functional con-nectivity (from resting-state functional MRI), glucose hypometabolism (from FDG-PET), and patholog-ical features (from histological stainings) – all currently under processing at CIBM. Tractography is a cutting-edge methodology to assess brain connectivity and the pipeline created could be further devel-oped to improve understanding and support diffusion metrics. The relationship between white and gray matter will also improve the understanding of spatio-temporal degeneration and the progression nature of the disease.O mecanismo que desencadeia a doença de Alzheimer (DA) não é bem conhecido, contudo sabe-se que a presença de placas amilóides e de emaranhados neurofibrilares da proteína tau, microgliose e ainda hipometabolismo de glucose estão envolvidos na fase inicial da cascata de desenvolvimento da doença. A principal vantagem dos modelos animais é justamente a possibilidade de estudar individualmente o impacto de cada um destes mecanismos no processo de neurodegeneração. Após uma injeção intracere-broventricular (icv) de estreptozotocina (STZ), várias espécies de animais mostraram um metabolismo anormal de glucose no cérebro, processo que foi referido como “diabetes do cérebro”. Vários estudos demonstraram que animais icv-STZ são portadores de características típicas de DA, mas este modelo animal nunca foi estudado em termos de biomarcadores derivados de técnicas de imagem por ressonân-cia magnética (IRM), exceto atrofia estrutural do cérebro. Um biomarcador promissor de DA que se acredita preceder a atrofia do córtex cerebral é a degeneração da matéria branca do cérebro, uma vez que foi fortemente correlacionado com a progressão e gravidade da doença. Logo, este projeto propõe um estudo longitudinal da degeneração da matéria branca em ratazanas icv-STZ utilizando IRM de di-fusão. O plano de processamento de imagem existente foi utilizado primeiramente para obter resultados preliminares e viabilizar a proposta de estratégias de otimização da mesma, em termos de melhoramento da qualidade de imagem e credibilidade das variáveis extraídas das imagens resultantes. Estas estratégias foram testadas e implementadas no plano de processamento quando a sua performance confirmou ser de valor, para que os resultados fossem o mais reproduzíveis possível em caracterizar a distribuição espácio-temporal da degeneração do cérebro neste modelo animal. Todos os procedimentos aqui descritos foram aprovados pelo serviço local dos assuntos veterinários. Ratazanas macho Wistar (N=18, 236±11 g) foram submetidas a uma injeção icv de STZ (3 mg/kg) no caso do grupo infetado (N=10) ou de um buffer no caso do grupo de controlo (N=8). As ratazanas foram examinadas no scanner de IRM do tipo Varian de 14 T em quatro momentos no tempo: 2, 6, 13 e 21 semanas após a injeção. As imagens por difusão foram adquiridas com uma sequência semi-adiabática spin-echo EPI PGSE com os seguintes parâmetros: 4 (b=0), 12 (b=0.8 ms/μm2), 16 (b=1.3 ms/μm2) and 30 (b=2 ms/μm2) direções; TE/TR=48/2500 ms, 9 secções coronais de 1 mm, δ/Δ=4/27 ms, FOV=23x17 mm2, matriz=128x64 e 4 shots. O plano existente de processamento de imagem incluía a correção das imagens ao nível de ruído e correntes-eddy. Posteriormente, os tensores de difusão e curtose foram estimados para cada voxel e os mapas paramétricos de anisotropia fracional (FA), difusão média, axial e radial (MD, AD e RD) e cur-tose média, axial e radial (MK, AK e RK) foram calculados. Adicionalmente, um modelo de difusão de água nas fibras da matéria branca foi utilizado para providenciar maior especificidade ao estudo da microestrutura do cérebro. Como tal, o modelo de dois compartimentos denominado WMTI-Watson foi também estimado e as seguintes variáveis foram derivadas do mesmo: a fração do volume de água , a difusividade paralela intra-axonal , as difusividades paralela ,║ e perpendicular ,ꓕ extra-axonais e, finalmente, a orientação da dispersão axonal 2. Este modelo matemático tem duas soluções possíveis dada a sua natureza quadrática, pelo que a solução >,║ foi imposta com base em evidências re-centes. Considerando estudos anteriores, as regiões de interesse (RDIs) da matéria branca escolhidas para analisar a microestrutura cerebral foram o corpo caloso, o cíngulo, a fimbria e a fórnix. Estes foram automaticamente segmentados através de registo de imagem de um atlas das regiões do cérebro da rata-zana e as médias das medidas extraídas dos tensores de difusão e curtose e ainda do modelo biofísico neuronal foram calculadas em cada RDI para cada conjunto de imagens obtidas. Os dois grupos de teste e controlo foram comparados usando testes t de Student bilaterais em cada momento do tempo, e a comparação das alterações longitudinais em cada grupo foi feita usando uma ANOVA. Devido ao baixo número de amostras, o último momento no tempo às 21 semanas foi excluído da análise. No decorrer deste projeto, várias estratégias para otimizar o processamento de imagem ou comple-mentar a análise da informação disponível foram testadas. Nomeadamente, o atlas cerebral da ratazana foi aperfeiçoado relativamente às regiões de matéria branca, a segmentação do cérebro foi testada com algoritmos automáticos e a correção do bias field em imagens estruturais de IRM foi adicionada ao plano antes do registo de imagem. O aumento dos ventrículos cerebrais é uma característica frequente em animais icv-STZ, constituindo um problema de alinhamento nos métodos de registo de imagem. No sentido de otimizar a correspondência entre as regiões do atlas e as respetivas regiões na imagem estru-tural e por difusão, vários procedimentos de registo de imagem foram testados. O co-registo de imagem convencional utiliza imagens estruturais para normalizar o espaço das imagens por difusão, no entanto os mapas paramétricos de FA têm vindo a substituir este conceito dado o excelente contraste que provi-denciam entre a matéria branca e cinzenta do cérebro. Mapas de FA com diferentes direções predomi-nantes mostraram uma melhoria significante da segmentação do corpo caloso e da fimbria e também do poder estatístico das variáveis extraídas destas RDIs. Adicionalmente, um novo plano de processamento de tratografia foi construído de raiz no âmbito deste projeto para extrair variáveis adicionais das fibras de interesse e compará-las com as variáveis de difusão obtidas por análise voxel-a-voxel. Por último, as variáveis calculadas através dos tensores de difusão e curtose foram avaliadas na matéria cinzenta do cérebro para uma caracterização espácio-temporal da degeneração cerebral na DA. Os resultados da análise estatística foram obtidos após integrar no plano de processamento as estra-tégias que mostraram valorizar o projeto em termos de qualidade de imagem ou credibilidade das vari-áveis. Houve poucas diferenças significativas ao longo do tempo em cada grupo, no entanto as diferen-ças entre grupos foram bastante acentuadas. As alterações ao nível da microestrutura da matéria branca foram consistentes com estudos prévios em animais icv-STZ usando métodos histológicos e avaliações das suas capacidades cognitivas. Alterações nas variáveis extraídas dos tensores indicaram deficiência axonal inicial na fimbria e no fórnix 2 semanas após injeção no grupo de teste, um potencial período de recuperação às 6 semanas e novamente deficiência axonal às 13 semanas, sendo que neste período tardio todas as RDIs foram afetadas. O modelo biofísico WMTI-Watson confirmou aumentar especificidade ao estudo da microestrutura, visto que demostrou danos intra-axonais na fimbria e no corpo caloso 2 semanas após injeção, seguidos de um período de recuperação e de perda de estrutura axonal definitiva às 13 semanas em todas as RDIs. Não só estes dois métodos de análise de IRM de difusão se complementam, como são também con-sistentes entre eles e com as tendências de alterações ao longo do tempo descritas noutros estudos. Além disso, o animal icv-STZ mostrou alterações características da DA, mesmo tendo a degeneração cerebral sido induzida pela disrupção do metabolismo de glucose no cérebro. Como tal, este modelo animal é excelente para reproduzir a doença e deverá continuar a ser avaliado nas diferentes áreas multidiscipli-nares para explorar a hipótese de a DA ser desencadeada pela falha do sistema insulina/glucose. A com-binação da informação de difusão obtida dos tensores e da modelação da difusão neuronal provou ser uma ferramenta promissora no estudo das fibras da matéria branca do cérebro e poderá vir a ser o desafio futuro no que toca a investigação clínica da DA. Este estudo focar-se-á em correlacionar as alterações microestruturais aqui descritas com dados de conectividade funcional (obtida por IRM funcional em repouso), hipometabolismo de glucose (por FDG-PET) e outras características patológicas (por colora-ção histológica) – todos já em curso no CIBM. Tratografia é a metodologia topo de gama para aceder à conetividade cerebral e o plano de processamento gerado neste projeto poderá continuar a ser desenvol-vido no futuro para informação adicional, assim como a relação entre a matéria branca e cinzenta poderá suplementar a compreensão da progressão da doença no espaço e no tempo

Towards an efficient segmentation of small rodents brain: a short critical review

One of the most common tasks in small rodents MRI pipelines is the voxel-wise segmentation of the volume in multiple classes. While many segmentation schemes have been developed for the human brain, fewer are available for rodent MRI, often by adaptation from human neuroimaging. Common methods include atlas-based and clustering schemes. The former labels the target volume by registering one or more pre-labeled atlases using a deformable registration method, in which case the result depends on the quality of the reference volumes, the registration algorithm and the label fusion approach, if more than one atlas is employed. The latter is based on an expectation maximization procedure to maximize the variance between voxel categories, and is often combined with Markov Random Fields and the atlas based approach to include spatial information, priors, and improve the classification accuracy. Our primary goal is to critically review the state of the art of rat and mouse segmentation of neuro MRI volumes and compare the available literature on popular, readily and freely available MRI toolsets, including SPM, FSL and ANTs, when applied to this task in the context of common pre-processing steps. Furthermore, we will briefly address the emerging Deep Learning methods for the segmentation of medical imaging, and the perspectives for applications to small rodents

INVESTIGATION OF RADIATION INJURY IN THE ESOPHAGUS FROM DEFINITIVE CHEMORADIATION THERAPY USING NOVEL IMAGING BIOMARKERS

Radiation injury in the esophagus occurs with high frequency from the treatment of non-small cell lung cancer (NSCLC). Radiation esophagitis is an acute normal tissue toxicity that negatively affects treatment efficacy by limiting dose and potentially interrupting radiation therapy. Clinical quantification of this toxicity is typically achieved by utilizing physician grading scales, assigning complication severity on an ordinal scale of symptom presentation and/or physician chosen interventions. These criteria are subjective in nature, both from the physician assigning the grade and the patient reporting the symptom. Furthermore, radiation therapy planning guidelines for the esophagus are derived from toxicity prediction models utilizing these subjective grading scores as complication endpoints. Not only does this schema of toxicity analysis leads to a lack of consistency between models from different study populations, and thereby radiation therapy planning recommendations for the esophagus, but inherent patient radiosensitivity is not considered, leading to suboptimal treatment regimens. The purpose of this work was to investigate radiation injury in the esophagus by first developing in-vivo imaging biomarkers of radiation-response in the esophagus using 4-dimensional computed tomography (4DCT) and 18fluorodeoxyglucose positron emission tomography (FDG-PET), separately. These imaging biomarkers were then compare with radiation esophagitis grade, using traditional and machine learning techniques, and shown to objectively quantify esophageal radiation toxicity. Metrics describing the esophageal radiation response from either imaging modality were strong classifiers of radiation esophagitis grade. Multivariate models to predict maximum esophagitis treatment grade (4DCT), and esophagitis symptom progression (FDG-PET) were developed and had strong performance for both scenarios. These imaging biomarkers were then used to comprehensively investigate the influence of dose-geometry and radiation type (photon or proton) on esophageal response. Using these radiation-response biomarkers in esophageal dose-response analysis, dose metrics with spatial information of esophageal dose coverage, (e.g. dose to a subregion of the esophagus with specific percent cross-sectional area coverage), as well as without spatial information, (traditional dose-volume histogram), was analyzed separately using machine learning methods. No detectable difference in response was observed when comparing dose metrics with and without spatial information. Statistical analysis showed no significant difference (p Inherent patient radiation sensitivity was investigated using esophageal expansion and delivered dose to the corresponding esophageal subregion. Cluster analysis was used to group patient patients based on their maximum expansion and delivered dose to the analyzed subregion of the esophagus. Patients clustered with proportionally higher expansion per delivered dose were considered radiosensitive. These results were then applied to NTCP toxicity modelling by using patient radiosensitivity cluster membership as a predictor variable. Models with the radiosensitive predictor outperformed models not including the cluster membership variable for prediction of grade 3 esophagitis

INVESTIGATION OF RADIATION INJURY IN THE ESOPHAGUS FROM DEFINITIVE CHEMORADIATION THERAPY USING NOVEL IMAGING BIOMARKERS

Radiation injury in the esophagus occurs with high frequency from the treatment of non-small cell lung cancer (NSCLC). Radiation esophagitis is an acute normal tissue toxicity that negatively affects treatment efficacy by limiting dose and potentially interrupting radiation therapy. Clinical quantification of this toxicity is typically achieved by utilizing physician grading scales, assigning complication severity on an ordinal scale of symptom presentation and/or physician chosen interventions. These criteria are subjective in nature, both from the physician assigning the grade and the patient reporting the symptom. Furthermore, radiation therapy planning guidelines for the esophagus are derived from toxicity prediction models utilizing these subjective grading scores as complication endpoints. Not only does this schema of toxicity analysis leads to a lack of consistency between models from different study populations, and thereby radiation therapy planning recommendations for the esophagus, but inherent patient radiosensitivity is not considered, leading to suboptimal treatment regimens. The purpose of this work was to investigate radiation injury in the esophagus by first developing in-vivo imaging biomarkers of radiation-response in the esophagus using 4-dimensional computed tomography (4DCT) and 18fluorodeoxyglucose positron emission tomography (FDG-PET), separately. These imaging biomarkers were then compare with radiation esophagitis grade, using traditional and machine learning techniques, and shown to objectively quantify esophageal radiation toxicity. Metrics describing the esophageal radiation response from either imaging modality were strong classifiers of radiation esophagitis grade. Multivariate models to predict maximum esophagitis treatment grade (4DCT), and esophagitis symptom progression (FDG-PET) were developed and had strong performance for both scenarios. These imaging biomarkers were then used to comprehensively investigate the influence of dose-geometry and radiation type (photon or proton) on esophageal response. Using these radiation-response biomarkers in esophageal dose-response analysis, dose metrics with spatial information of esophageal dose coverage, (e.g. dose to a subregion of the esophagus with specific percent cross-sectional area coverage), as well as without spatial information, (traditional dose-volume histogram), was analyzed separately using machine learning methods. No detectable difference in response was observed when comparing dose metrics with and without spatial information. Statistical analysis showed no significant difference (p Inherent patient radiation sensitivity was investigated using esophageal expansion and delivered dose to the corresponding esophageal subregion. Cluster analysis was used to group patient patients based on their maximum expansion and delivered dose to the analyzed subregion of the esophagus. Patients clustered with proportionally higher expansion per delivered dose were considered radiosensitive. These results were then applied to NTCP toxicity modelling by using patient radiosensitivity cluster membership as a predictor variable. Models with the radiosensitive predictor outperformed models not including the cluster membership variable for prediction of grade 3 esophagitis

ADVANCEMENTS IN QUANTITATIVE PERFUSION MAGNETIC RESONANCE IMAGING (MRI) OF DEMENTIA

Alzheimer's disease (AD) affects a considerable, and increasing, part of the population. Early diagnosis of AD is very important to permit effective therapy, and minimize AD's social and economic burden. The goal of our research is to evaluate the changes of cerebral perfusion (i.e., blood flow) in the early stages of AD and the effects from hypertension.We studied volunteers with Mild Cognitive Impairment (MCI) and early AD from the Pittsburgh cohort of the Cardiovascular Health Study (CHS) Cognitive Study during a four-year follow-up. Previously, studies used referral patients who typically have more advanced AD. No perfusion data concerning the early and transitional disease stages are currently available from population studies (i.e., subjects who have been monitored longitudinally in time). There are no common techniques for perfusion quantification and image analysis so that inconsistencies are observed between/within studies, modalities, and researchers. Several advancements were achieved in preparation for the cohort study. First, we improved the accuracy and speed of brain perfusion quantification. Second, we improved the accuracy of image registration to a reference brain using quantitative validation of a registration method and performance comparison with a popular registration method. Third, we improved the method of statistical analysis for evaluating the changes of perfusion between groups. Fourth, we evaluated the changes of cerebral perfusion between cognitive groups (controls, MCIs, ADs), and hypertension and normo-tensive subgroups.Individual perfusion maps were improved by measuring and incorporating individual arrival time, saturation effects, and individual inversion efficiency. A fully deformable registration technique was shown to be more accurate than standard techniques like statistical parametric mapping to detect local perfusion changes. All of the published literature for perfusion up-to-date reported decreased perfusion in AD, but we found hyperperfusion in some regions. The regional findings imply that a hemodynamic process, at the capillary level, accompanied the neurodegenerative process. Hypertensive normal cognitive controls demonstrated hypoperfusion in regions usually involved in AD pathology. However, the effect of hypertension was attenuated after the onset of the pathological cognitive process

Quantitation in MRI : application to ageing and epilepsy

Multi-atlas propagation and label fusion techniques have recently been developed for segmenting the human brain into multiple anatomical regions. In this thesis, I investigate possible adaptations of these current state-of-the-art methods. The aim is to study ageing on the one hand, and on the other hand temporal lobe epilepsy as an example for a neurological disease. Overall effects are a confounding factor in such anatomical analyses. Intracranial volume (ICV) is often preferred to normalize for global effects as it allows to normalize for estimated maximum brain size and is hence independent of global brain volume loss, as seen in ageing and disease. I describe systematic differences in ICV measures obtained at 1.5T versus 3T, and present an automated method of measuring intracranial volume, Reverse MNI Brain Masking (RBM), based on tissue probability maps in MNI standard space. I show that this is comparable to manual measurements and robust against field strength differences. Correct and robust segmentation of target brains which show gross abnormalities, such as ventriculomegaly, is important for the study of ageing and disease. We achieved this with incorporating tissue classification information into the image registration process. The best results in elderly subjects, patients with TLE and healthy controls were achieved using a new approach using multi-atlas propagation with enhanced registration (MAPER). I then applied MAPER to the problem of automatically distinguishing patients with TLE with (TLE-HA) and without (TLE-N) hippocampal atrophy on MRI from controls, and determine the side of seizure onset. MAPER-derived structural volumes were used for a classification step consisting of selecting a set of discriminatory structures and applying support vector machine on the structural volumes as well as morphological similarity information such as volume difference obtained with spectral analysis. Acccuracies were 91-100 %, indicating that the method might be clinically useful. Finally, I used the methods developed in the previous chapters to investigate brain regional volume changes across the human lifespan in over 500 healthy subjects between 20 to 90 years of age, using data from three different scanners (2x 1.5T, 1x 3T), using the IXI database. We were able to confirm several known changes, indicating the veracity of the method. In addition, we describe the first multi-region, whole-brain database of normal ageing

A hitchhiker's guide to diffusion tensor imaging

Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on: acquisition protocol, artifact handling, data quality control, reconstruction algorithm, and visualization approaches, and quantitative analysis methodology. Furthermore, the researcher and/or clinician also needs to take into account and decide on the most suited software tool(s) for each stage of the DTI analysis pipeline. Herein, we provide a straightforward hitchhiker's guide, covering all of the workflow's major stages. Ultimately, this guide will help newcomers navigate the most critical roadblocks in the analysis and further encourage the use of DTI.The work was supported by SwitchBox-FP7-HEALTH-2010-grant 259772-2. The authors acknowledge Nadine Santos for her help in editing the manuscript

Feasibility of using Arterial Spin Labeling for Detecting Longitudinal Changes in Cerebral Blood Flow

The ability of the perfusion MRI technique, arterial spin labeling (ASL), to quantify cerebral blood flow (CBF) makes it attractive for longitudinal studies of changes in brain function, such as those related to chronic pain. However, ASL\u27s poor spatial resolution makes image alignment between sessions difficult, leading to increased variance and greater Type-I errors. In addition, variability due to differences in basal blood flow between sessions and confounding effects such as the arterial transit time (ATT) have the potential to reduce reproducibility over time. The focus of this thesis is to investigate the ability of ASL to detect long-term changes in regional CBF within an individual on a voxel-wise level. It is hypothesized that ASL has the sensitivity to detect activation-induced CBF changes over periods as long as a month if the sources of variance that degrade between-session comparisons are minimized. To test this hypothesis rest and activation (motor task) CBF images were acquired from healthy subjects on three separate imaging sessions. Registration errors were minimized by using individual head molds to replicate the head position in successive sessions. Variations in resting CBF were controlled for by performing the imaging during the same time of day, and subjects were asked to refrain from using common substances, such as caffeine, that are known to affect CBF. Finally, ATT maps were generated on each session to investigate its stability. From these data sets, the within- and between-session variability in CBF was determined and motor-related activation maps were generated from rest and activation data acquired on from the same session and from sessions separated by a week and a month. The results demonstrated excellent reliability (intraclass correlation coefficients greater than 0.75) both within- (0.89 ± 0.2) and between-session (0.84 ± 0.15), and high reproducibility (within subject coefficient of variation, wsCV, greater than 20%) within- (wsCV = 4.7 ± 4.5%) and between-session (wsCV = 5.7 ± 4.4%). Between-session reproducibility of the ATT was high (wsCV = 5.0 ± 2.7%), suggesting that the confounding effect of ATT over a month was minimal. The similarity in within- and between-session variability and their activation maps indicated that registration errors between sessions were minimal. Measures of precision of activation demonstrated that less than ~20% of between-session activation were false positives. These results demonstrate the feasibility of conducting voxel-wise analysis of CBF images acquired on different days and highlight the potential of this technique for longitudinal studies

Automated Extraction of Biomarkers for Alzheimer's Disease from Brain Magnetic Resonance Images

In this work, different techniques for the automated extraction of biomarkers for Alzheimer's disease (AD) from brain magnetic resonance imaging (MRI) are proposed. The described work forms part of PredictAD (www.predictad.eu), a joined European research project aiming at the identification of a unified biomarker for AD combining different clinical and imaging measurements. Two different approaches are followed in this thesis towards the extraction of MRI-based biomarkers: (I) the extraction of traditional morphological biomarkers based on neuronatomical structures and (II) the extraction of data-driven biomarkers applying machine-learning techniques. A novel method for a unified and automated estimation of structural volumes and volume changes is proposed. Furthermore, a new technique that allows the low-dimensional representation of a high-dimensional image population for data analysis and visualization is described. All presented methods are evaluated on images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), providing a large and diverse clinical database. A rigorous evaluation of the power of all identified biomarkers to discriminate between clinical subject groups is presented. In addition, the agreement of automatically derived volumes with reference labels as well as the power of the proposed method to measure changes in a subject's atrophy rate are assessed. The proposed methods compare favorably to state-of-the art techniques in neuroimaging in terms of accuracy, robustness and run-time