Investigating white matter changes underlying overactive bladder in multiple sclerosis with diffusion MRI

Lower urinary tract symptoms (LUTS) are presented in more than 80% of multiple sclerosis (MS) patients. Current understanding of LUT control is based on studies exploring activities in grey matter (GM) and investigating functional correlations with LUTS. The relationship between white matter (WM) changes and overactive bladder (OAB) symptoms are limited to findings in small vessel disease, and the nature of the association between WM changes and OAB symptoms is poorly understood. Advanced diffusion-weighted magnetic resonance imaging (MRI) techniques provide non-invasive techniques to study WM abnormalities and correlates to clinical observations. The overarching objectives of this work are to explore WM abnormalities subtending OAB symptoms in MS, and to reconstruct the structural network underpinning the working model of lower urinary tract (LUT) control. Using Tract-Based Spatial Statistics (TBSS), OAB symptoms related WM abnormalities in MS can be identified, and a structural network subtending OAB symptoms in MS can be subsequently created. The findings of this work illustrate the correlation between OAB symptoms severity and WM abnormalities in MS. These were observed in regions in frontal lobes and non-dominant hemisphere, including corpus callosum, anterior corona radiata bilaterally, right anterior thalamic radiation, superior longitudinal fasciculus bilaterally, and right inferior longitudinal fasciculus. The structural network created for OAB symptoms in MS connected regions known to be involved in the working model of LUT control, and the network identified connectivity between insula and frontal lobe, which is the key circuit for perception of bladder fullness. Moreover, structural connectivity between insula-temporal lobe and insula-occipital lobe were observed, which may underpin changes seen in functional MRI (fMRI) studies. The novel findings of this study present WM abnormalities and structural connectivity subtending LUTS in MS with diffusion-weighted imaging (DWI). The techniques used in this work can be applied to other patterns of LUTS and other neurological diseases

Development of Methodologies for Diffusion-weighted Magnetic Resonance Imaging at High Field Strength

Diffusion-weighted imaging of small animals at high field strengths is a challenging prospect due to its extreme sensitivity to motion. Periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) was introduced at 9.4T as an imaging method that is robust to motion and distortion. Proton density (PD)-weighted and T2-weighted PROPELLER data were generally superior to that acquired with single-shot, Cartesian and echo planar imaging-based methods in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio and resistance to artifacts. Simulations and experiments revealed that PROPELLER image quality was dependent on the field strength and echo times specified. In particular, PD-weighted imaging at high field led to artifacts that reduced image contrast. In PROPELLER, data are acquired in progressively rotated blades in k-space and combined on a Cartesian grid. PROPELLER with echo truncation at low spatial frequencies (PETALS) was conceived as a postprocessing method that improved contrast by reducing the overlap of k-space data from different blades with different echo times. Where the addition of diffusion weighting gradients typically leads to catastrophic motion artifacts in multi-shot sequences, diffusion-weighted PROPELLER enabled the acquisition of high quality, motion-robust data. Applications in the healthy mouse brain and abdomen at 9.4T and in stroke patients at 3T are presented. PROPELLER increases the minimum scan time by approximately 50%. Consequently, methods were explored to reduce the acquisition time. Two k-space undersampling regimes were investigated by examining image fidelity as a function of degree of undersampling. Undersampling by acquiring fewer k-space blades was shown to be more robust to motion and artifacts than undersampling by expanding the distance between successive phase encoding steps. To improve the consistency of undersampled data, the non-uniform fast Fourier transform was employed. It was found that acceleration factors of up to two could be used with minimal visual impact on image fidelity. To reduce the number of scans required for isotropic diffusion weighting, the use of rotating diffusion gradients was investigated, exploiting the rotational symmetry of the PROPELLER acquisition. Fixing the diffusion weighting direction to the individual rotating blades yielded geometry and anisotropy-dependent diffusion measurements. However, alternating the orientations of diffusion weighting with successive blades led to more accurate measurements of the apparent diffusion coefficient while halving the overall acquisition time. Optimized strategies are proposed for the use of PROPELLER in rapid high resolution imaging at high field strength

Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging

The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.Comment: 69 pages, 6 figures, 1 tabl

Studying neuroanatomy using MRI

The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging, and disease. Developments in MRI acquisition, image processing, and data modelling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and inferring microstructural properties; we also describe key artefacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, though methods need to improve and caution is required in its interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works

Nerve diffusion tensor imaging

Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that in vivo visualises random translational movement of water molecules. DTI has inherent difficulties with low signal-to-noise ratio, sensitivity to patient motion, field inhomogeneities and fast T2 relaxation. It has been used in the central nervous system, although it has not been assessed in the peripheral nervous system. The aim of this thesis was to investigate if DTI in peripheral nerves was feasible, and if so, to investigate clinical implications. Study I showed that in healthy volunteers the peripheral nerves, the sciatic nerves, could be visualised in vivo using DTI and fiber tracking. Study II showed that sciatic nerves, including their division into the tibial and common fibular nerves, have a characteristic diffusion pattern with most impaired diffusion perpendicular to the nerve direction. This allowed nerves, excluding other tissues and artifacts, to be visualised using a novel approach called diffusion-direction- dependent imaging and with a simple unidirectional diffusion maximum-intensity projection approach. Study III showed that the olfactory bulbs (OBs) and olfactory tracts could be visualised in vivo using DTI and fiber tracking. In Study IV, Parkinson’s disease (PD) patients with impaired olfaction were evaluated with DTI of the OBs. A novel approach of DTI was used, taking advantage of the technique’s inherent directional information, for region of interest placement and diffusion measurements in the OBs. In the PD patient group diffusion was altered in the OBs, compared to healthy controls. This was hypothesised, since α-synuclein inclusions and Lewy neurites interfering with nerve structure have been detected in the OBs. However, the coefficient of variation between two identical DTI series was high, due to the small size of the OBs and their location in an area susceptible to artifacts, and the difference between the groups was statistically significant only for the first of two series. In Study V, patients of the Swedish ‘Huddinge Spinocerebellar ataxia (SCA) Family’ with peripheral neuropathy, were evaluated with DTI of a peripheral nerve. Diffusion alterations were found in peripheral nerves in SCA patients, compared to healthy controls, which was statistically significant. In conclusion, DTI in peripheral nerves is feasible and can be used to detect diffusion alterations in OBs in PD patients and in peripheral nerves in SCA patients with peripheral neuropathy

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

Optimizing Magnetic Resonance Imaging for Image-Guided Radiotherapy

Magnetic resonance imaging (MRI) is playing an increasingly important role in image-guided radiotherapy. MRI provides excellent soft tissue contrast, and is flexible in characterizing various tissue properties including relaxation, diffusion and perfusion. This thesis aims at developing new image analysis and reconstruction algorithms to optimize MRI in support of treatment planning, target delineation and treatment response assessment for radiotherapy. First, unlike Computed Tomography (CT) images, MRI cannot provide electron density information necessary for radiation dose calculation. To address this, we developed a synthetic CT generation algorithm that generates pseudo CT images from MRI, based on tissue classification results on MRI for female pelvic patients. To improve tissue classification accuracy, we learnt a pelvic bone shape model from a training dataset, and integrated the shape model into an intensity-based fuzzy c-menas classification scheme. The shape-regularized tissue classification algorithm is capable of differentiating tissues that have significant overlap in MRI intensity distributions. Treatment planning dose calculations using synthetic CT image volumes generated from the tissue classification results show acceptably small variations as compared to CT volumes. As MRI artifacts, such as B1 filed inhomogeneity (bias field) may negatively impact the tissue classification accuracy, we also developed an algorithm that integrates the correction of bias field into the tissue classification scheme. We modified the fuzzy c-means classification by modeling the image intensity as the true intensity corrupted by the multiplicative bias field. A regularization term further ensures the smoothness of the bias field. We solved the optimization problem using a linearized alternating direction method of multipliers (ADMM) method, which is more computational efficient over existing methods. The second part of this thesis looks at a special MR imaging technique, diffusion-weighted MRI (DWI). By acquiring a series of DWI images with a wide range of b-values, high order diffusion analysis can be performed using the DWI image series and new biomarkers for tumor grading, delineation and treatment response evaluation may be extracted. However, DWI suffers from low signal-to-noise ratio at high b-values, and the multi-b-value acquisition makes the total scan time impractical for clinical use. In this thesis, we proposed an accelerated DWI scheme, that sparsely samples k-space and reconstructs images using a model-based algorithm. Specifically, we built a 3D block-Hankel tensor from k-space samples, and modeled both local and global correlations of the high dimensional k-space data as a low-rank property of the tensor. We also added a phase constraint to account for large phase variations across different b-values, and to allow reconstruction from partial Fourier acquisition, which further accelerates the image acquisition. We proposed an ADMM algorithm to solve the constrained image reconstruction problem. Image reconstructions using both simulated and patient data show improved signal-to-noise ratio. As compared to clinically used parallel imaging scheme which achieves a 4-fold acceleration, our method achieves an 8-fold acceleration. Reconstructed images show reduced reconstruction errors as proved on simulated data and similar diffusion parameter mapping results on patient data.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143919/1/llliu_1.pd

Moving beyond DTI: non-gaussian diffusion in the brain and skeletal muscle

Diffusion Magnetic Resonance Imaging (dMRI) is a diagnostic technique able to provide in- vivo measures that are related to the microstructure of tissues. Thanks to the sensitivity to microstructural tissue changes, Diffusion Weighted Imaging (DWI) and derived metrics, as the Apparent Diffusion Coefficient (ADC) , became the gold standard for the detection of strokes and ischemia since the early 90‟s. In 1994 Basser and colleagues introduced Diffusion Tensor Imaging (DTI), the first quantification approach able to capture the anisotropy of the diffusion process in in-vivo biological tissues. Chapter II shows the results we obtained applying DTI to investigate white matter alterations of a population affected by Friedreich‟s Ataxia. After more than 20 years from its introduction, DTI is still widely applied. However, concerns about the limitations of the technique have been increasingly risen over-time, with particular reference to the lack of specificity of the model and the coexistence of tissues with multiple architectures. Additionally, the tensor model can be applied only to a range of “moderate” diffusion sensitizations, after which the presence of biological membranes becomes non-negligible and gives origin to phenomena of “non-Gaussian diffusion”, that violate the assumptions of the model. Chapter III and Chapter IV deal specifically with these limitations, addressing the problem with two different approaches and applications. Another popular technique to investigate the dMRI signal is Spherical Deconvolution (SD), that in Chapter IV is presented in a tissue specific formulation and applied to derive diffusivity metrics specific to white matter, gray matter and cerebrospinal fluid, both in healthy controls and in a patient affected by MS. Since the early days of dMRI, experiments have been performed not only in the brain but in several body districts, including the skeletal muscle. Back in 1986 Le Bihan et al. observed that the water flowing in the micro vascular network and in the vessels was contributing to the acquisition of data at very low diffusion sensitization, and proposed the “Intra- Voxel Incoherent Motion” (IVIM) model. IVIM can be seen either as a model to obtain measures of pseudo-diffusion, or as a technique to obtain perfusion free ADC measures, thus recognizing it as an artifact. Although dMRI and DTI were applied to the skeletal muscle since its early days, later evolutions as Diffusion Kurtosis Imaging have only recently been applied to the skeletal muscle to fit dMRI data acquired at strong diffusion sensitization. The concepts of IVIM and DKI are developed in Chapter V, where the effects of the first on DTI and DKI, as well as the relation between DTI and DKI metrics are investigated through simulations and MRI data of the calf. In line with the current dMRI literature, the first 5 chapters of this thesis depict the diffusion signal as a complex measure arising from multiple tissue components. Chapter VI investigates a multi-compartment pseudo-continuous deconvolution approach, a technique that does not require explicit modeling of the tissues. Finally, Chapter VII presents an overview of other research topics I have work on during the PhD

Fast diffusion MRI based on sparse acquisition and reconstruction for long-term population imaging

Diffusion weighted magnetic resonance imaging (dMRI) is a unique MRI modality to probe the diffusive molecular transport in biological tissue. Due to its noninvasiveness and its ability to investigate the living human brain at submillimeter scale, dMRI is frequently performed in clinical and biomedical research to study the brain’s complex microstructural architecture. Over the last decades large prospective cohort studies have been set up with the aim to gain new insights into the development and progression of brain diseases across the life span and to discover biomarkers for disease prediction and potentially prevention. To allow for diverse brain imaging using different MRI modalities, stringent scan time limits are typically imposed in population imaging. Nevertheless, population studies aim to apply advanced and thereby time consuming dMRI protocols that deliver high quality data with great potential for future analysis. To allow for time-efficient but also versatile diffusion imaging, this thesis contributes to the investigation of accelerating diffusion spectrum imaging (DSI), an advanced dMRI technique that acquires imaging data with high intra-voxel resolution of tissue microstructure. Combining state-of-the-art parallel imaging and the theory of compressed sensing (CS) enables the acceleration of spatial encoding and diffusion encoding in dMRI. In this way, the otherwise long acquisition times in DSI can be reduced significantly. In this thesis, first, suitable q-space sampling strategies and basis functions are explored that fulfill the requirements of CS theory for accurate sparse DSI reconstruction. Novel 3D q-space sample distributions are investigated for CS-DSI. Moreover, conventional CS-DSI based on the discrete Fourier transform is compared for the first time to CS-DSI based on the continuous SHORE (simple harmonic oscillator based reconstruction and estimation) basis functions. Based on these findings, a CS-DSI protocol is proposed for application in a prospective cohort study, the Rhineland Study. A pilot study was designed and conducted to evaluate the CS-DSI protocol in comparison with state-of-the-art 3-shell dMRI and dedicated protocols for diffusion tensor imaging (DTI) and for the combined hindered and restricted model of diffusion (CHARMED). Population imaging requires processing techniques preferably with low computational cost to process and analyze the acquired big data within a reasonable time frame. Therefore, a pipeline for automated processing of CS-DSI acquisitions was implemented including both in-house developed and existing state-of-the-art processing tools. The last contribution of this thesis is a novel method for automatic detection and imputation of signal dropout due to fast bulk motion during the diffusion encoding in dMRI. Subject motion is a common source of artifacts, especially when conducting clinical or population studies with children, the elderly or patients. Related artifacts degrade image quality and adversely affect data analysis. It is, thus, highly desired to detect and then exclude or potentially impute defective measurements prior to dMRI analysis. Our proposed method applies dMRI signal modeling in the SHORE basis and determines outliers based on the weighted model residuals. Signal imputation reconstructs corrupted and therefore discarded measurements from the sparse set of inliers. This approach allows for fast and robust correction of imaging artifacts in dMRI which is essential to estimate accurate and precise model parameters that reflect the diffusive transport of water molecules and the underlying microstructural environment in brain tissue.Die diffusionsgewichtete Magnetresonanztomographie (dMRT) ist ein einzigartiges MRTBildgebungsverfahren, um die Diffusionsbewegung von Wassermolekülen in biologischem Gewebe zu messen. Aufgrund der Möglichkeit Schichtbilder nicht invasiv aufzunehmen und das lebende menschliche Gehirn im Submillimeter-Bereich zu untersuchen, ist die dMRT ein häufig verwendetes Bildgebungsverfahren in klinischen und biomedizinischen Studien zur Erforschung der komplexen mikrostrukturellen Architektur des Gehirns. In den letzten Jahrzehnten wurden große prospektive Kohortenstudien angelegt, um neue Einblicke in die Entwicklung und den Verlauf von Gehirnkrankheiten über die Lebenspanne zu erhalten und um Biomarker zur Krankheitserkennung und -vorbeugung zu bestimmen. Um durch die Verwendung unterschiedlicher MRT-Verfahren verschiedenartige Schichtbildaufnahmen des Gehirns zu ermöglich, müssen Scanzeiten typischerweise stark begrenzt werden. Dennoch streben Populationsstudien die Anwendung von fortschrittlichen und daher zeitintensiven dMRT-Protokollen an, um Bilddaten in hoher Qualität und mit großem Potential für zukünftige Analysen zu akquirieren. Um eine zeiteffizente und gleichzeitig vielseitige Diffusionsbildgebung zu ermöglichen, leistet diese Dissertation Beiträge zur Untersuchung von Beschleunigungsverfahren für die Bildgebung mittels diffusion spectrum imaging (DSI). DSI ist ein fortschrittliches dMRT-Verfahren, das Bilddaten mit hoher intra-voxel Auflösung der Gewebestruktur erhebt. Werden modernste Verfahren zur parallelen MRT-Bildgebung mit der compressed sensing (CS) Theorie kombiniert, ermöglicht dies eine Beschleunigung der räumliche Kodierung und der Diffusionskodierung in der dMRT. Dadurch können die ansonsten langen Aufnahmezeiten für DSI erheblich reduziert werden. In dieser Arbeit werden zuerst geeigenete Strategien zur Abtastung des q-space sowie Basisfunktionen untersucht, welche die Anforderungen der CS-Theorie für eine korrekte Signalrekonstruktion der dünnbesetzten DSI-Daten erfüllen. Neue 3D-Verteilungen von Messpunkten im q-space werden für die Verwendung in CS-DSI untersucht. Außerdem wird konventionell auf der diskreten Fourier-Transformation basierendes CS-DSI zum ersten Mal mit einem CS-DSI Verfahren verglichen, welches kontinuierliche SHORE (simple harmonic oscillator based reconstruction and estimation) Basisfunktionen verwendet. Aufbauend auf diesen Ergebnissen wird ein CS-DSI-Protokoll zur Anwendung in einer prospektiven Kohortenstudie, der Rheinland Studie, vorgestellt. Eine Pilotstudie wurde entworfen und durchgeführt, um das CS-DSI-Protokoll im Vergleich mit modernster 3-shell-dMRT und mit dedizierten Protokollen für diffusion tensor imaging (DTI) und für das combined hindered and restricted model of diffusion (CHARMED) zu evaluieren. Populationsbildgebung erfordert Prozessierungsverfahren mit möglichst geringem Rechenaufwand, um große akquirierte Datenmengen in einem angemessenen Zeitrahmen zu verarbeiten und zu analysieren. Dafür wurde eine Pipeline zur automatisierten Verarbeitung von CS-DSI-Daten implementiert, welche sowohl eigenentwickelte als auch bereits existierende moderene Verarbeitungsprogramme enthält. Der letzte Beitrag dieser Arbeit ist eine neue Methode zur automatischen Detektion und Imputation von Signalabfall, welcher durch schnelle Bewegungen während der Diffusionskodierung in der dMRT entsteht. Bewegungen der Probanden während der dMRT-Aufnahme sind eine häufige Ursache für Bildfehler, vor allem in klinischen oder Populationsstudien mit Kindern, alten Menschen oder Patienten. Diese Artefakte vermindern die Datenqualität und haben einen negativen Einfluss auf die Datenanalyse. Daher ist es das Ziel, fehlerhafte Messungen vor der dMRI-Analyse zu erkennen und dann auszuschließen oder wenn möglich zu ersetzen. Die vorgestellte Methode verwendet die SHORE-Basis zur dMRT-Signalmodellierung und bestimmt Ausreißer mit Hilfe von gewichteten Modellresidualen. Die Datenimputation rekonstruiert die unbrauchbaren und daher verworfenen Messungen mit Hilfe der verbleibenden, dünnbesetzten Menge an Messungen. Dieser Ansatz ermöglicht eine schnelle und robuste Korrektur von Bildartefakten in der dMRT, welche erforderlich ist, um korrekte und präzise Modellparameter zu schätzen, die die Diffusionsbewegung von Wassermolekülen und die zugrundeliegende Mikrostruktur des Gehirngewebes reflektieren