Quantitative Imaging of Water Transport and Relaxation Processes in the Brain and in Other Soft Tissues

大脑和其他软组织中水分运输和放松过程的定量成像

• 批准号: 10690356
• 负责人: PETER J. BASSER
• 金额: $ 133.43万
• 依托单位: EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10690356
• 关键词: Address; Aging; Anatomy; Anisotropy; Architecture; Axon; Biological; Biomedical Research; Biopsy; Brain; Brain Neoplasms; Caliber; Cells; Cellularity; Cerebral cortex; Clinic; Clinical; Color; Communities; Complex; Contrast Media; Data; Detection; Development; Diagnostic Neoplasm Staging; Diffuse; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Dyes; Evolution; Foundations; Fractals; Gel; Glean; Goals; Heterogeneity; Histologic; Histology; Hour; Image; Imaging Phantoms; Joints; Lead; Length; MRI Scans; Magnetic Resonance Imaging; Maps; Mathematics; Measures; Methodology; Methods; Microscopic; Modeling; Monitor; Multicenter Trials; Neurosciences; Neurosciences Research; Normal Statistical Distribution; Outcome; Pathologic; Pathology; Pathway interactions; Physiologic pulse; Population; Process; Property; Protons; Radial; Relaxation; Resolution; Scheme; Shapes; Signal Transduction; Source; Specimen; Stains; Statistical Distributions; Stroke; Structure; Techniques; Testing; Time; Tissues; Translating; Translations; Trauma; Uncertainty; Water; Work; bench to bedside; brain tissue; cancer diagnosis; cancer imaging; cell water; clinical diagnostics; clinically relevant; diffusion anisotropy; experimental study; extracellular; gray matter; imaging approach; imaging biomarker; imaging modality; improved; in vivo; innovation; interest; invention; mathematical model; migration; mild traumatic brain injury; neural; non-Gaussian model; novel; novel strategies; physical model; quantitative imaging; radiologist; reconstruction; soft tissue; tractography; tumor; water diffusion; white matter

We continue to invent, develop, and translate novel quantitative Magnetic Resonance Imaging (MRI) methods from "bench to bedside". Specifically, we explore new ways to assess tissue structure and architecture in vivo and non-invasively, primarily by "following the water", with the aim of enabling applications in the neurosciences and biomedical research communities, and translating these novel approaches to improve clinical outcomes. Diffusion Tensor MRI (DT-MRI or DTI) and DTI Tractography are the best-known imaging method we invented, developed, and successfully translated to the clinic. DTI is used to measure and map a diffusion tensor of mobile tissue water, and produces scalar parameters that are intrinsic to the tissues without the need to introduce exogenous contrast agents or dyes, but are obtained by following endogenous water protons residing within tissue. One DTI-derived quantity, the orientationally-averaged diffusion coefficient or mean ADC (mADC), successfully visualizes a stroke in progress, and is also widely used in cancer imaging to detect tumors, and then monitor changes in tumor cellularity following therapy, and in many other diseases and disorders, like TBI. Our development of novel diffusion anisotropy metrics, like the Fractional Anisotropy (FA) enabled white matter pathways to be visualized for the first time in vivo. The development direction-encoded color (DEC) maps of axon orientation allowed us to map the orientation of white matter pathways in the brain. To assess anatomical connectivity between different functional regions in the brain, we invented, proposed, and developed DTI streamline "tractography", made possible by a general mathematical framework we developed to continuously and smoothly approximate measured discrete, noisy, diffusion tensor field data. Collectively, these methods and approaches have enabled detailed anatomical and structural analyses of the brain in vivo, which was only possible previously using laborious, invasive histological or pathological methods performed on excised (dead) tissue specimen. As DTI started to migrate into large, multi-center trials and studies, we began developing a battery of quantitative statistical tests to determine the significance of region of interest (ROI) and population differences observed in DTI data. We developed empirical Monte Carlo and Bootstrap methods for determining features of the statistical distribution of the diffusion tensor from experimental DTI data and a novel tensor-variate Gaussian distribution that describes the variability of the diffusion tensor in an ideal DTI experiment. More recently, we developed approaches to measure uncertainties of many tensor-derived quantities using perturbation and statistical approaches. These innovations collectively provide the foundation for applying powerful statistical hypothesis tests to address a wide array of important biological and clinical questions that previously could only be tackled in an ad hoc manner, if at all. More recently, we have developed sophisticated mathematical/physical models of water diffusion profiles to relate these to the MR signals we measure. This activity enables us to "drill down into the voxel" to infer new microstructural and architectural features of tissue (primarily white matter in the brain). One example is our composite hindered and restricted model of diffusion (CHARMED) MRI framework to measure a mean axon radius within a pack of axons, and an estimate of the intra and extracellular volume fractions. A refinement of CHARMED, AxCaliber MRI, enabled us to measure the axon diameter distribution (ADD) within white matter pathways. Sophisticated multiple pulsed field gradient (mPFG) NMR and MRI sequences help us characterize microscopic anisotropy within tissues like gray matter, which are macroscopically isotropic (like a homogeneous gel). We have developed physical MRI phantoms to test and interrogate our various mathematical models describing water diffusion in complex tissues and infer distributions of size and shape of pores in biological tissue and other porous media from their MR data. Our group has applied novel fractal models to characterize anomalous diffusion processes that reveal underlying hierarchical structures. These also yield novel sources of MR contrast geared toward neuroscience applications, such as in vivo Brodmann or cytoarchitechtonic parcellation of the cerebral cortex or clinical diagnostic applications, such as mild TBI detection, improved cancer diagnosis or brain tumor staging. We have been developing ways to characterize non-Gaussian features of the net displacement distribution measured using MRI. To this end, our group continues to work on reconstructing the 'average propagator' (net displacement distribution) and features derived from it using a relatively small number of DWIs to facilitate their clinical migration. The average propagator is the "holy grail" of displacement or diffusion MR imaging, which subsumes DTI as well as other higher-order tensor (HOT) methods. One approach we used previously was an iterative reconstruction scheme along with a priori information and physical constraints to infer the average propagator from DWI data. Another approach was to use a CT-like reconstruction method to estimate the displacement profile from DWI data. The most successful method to date, however, uses a Hermite functional basis to represent the average propagator, which compresses the amount of DWI data required while providing a plethora of new imaging parameters or "stains" with which to characterize microstructural features in tissues. An extension of MAP-MRI, Time Scaling MRI, which characterizes the evolution of the average propagator with diffusion time, can help us glean features describing the hierarchical organization of tissue structures. A significant new initiative in our group has been the invention and development of several efficient and accurate 2D-MRI relaxometry/diffusometry/exchange methodologies. These include ways to measure joint distributions and correlations between diffusivity, T1 and T2, as well as exchange between and among various them. From the standpoint of microstructure imaging, these approaches provide increasing evidence of the existence of multiple distinct water components within neural tissue which have been previously undetectable using conventional MRI methods. To help migrate these approaches from bench to bedside, we have developed various means to dramatically reduce the amount of acquired MR data required to estimate these multidimensional distributions. Are first approach was to use compressed sensing, but more recently, we have developed novel ways to incorporate a priori information about these parameters, which vastly reduce the amount of data required with Marginal Distribution Constrained Optimization (MADCO) being the best example. Collectively, these novel methods and methodologies represent a clear path to realizing in vivo MRI histology and pathology--providing detailed microstructural and microarchitectural information about cells and tissues that otherwise could only be gleaned using laborious and invasive histological or pathological techniques applied on biopsied or excised specimens. We are migrating the field of "microstructure imaging" to "microstructure and microdynamic imaging", and in the process, are "making the invisible visible". Several new methods under development now include the successful launch of a pipeline to estimate a diffusion tensor distribution to characterize the heterogeneity of diffusive transport within each voxel.