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: 10915329
• 负责人: PETER J. BASSER
• 金额: $ 141.05万
• 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/10915329
• 关键词: Address; Aging; Anatomy; Anisotropy; Architecture; Axon; Biological; Biomedical Research; Biopsy; Brain; Brain Neoplasms; Cells; Cellularity; Cerebral cortex; Characteristics; Clinic; Clinical; Color; Communities; Complex; Contrast Media; Data; Data Science; Detection; Development; Diagnostic Neoplasm Staging; Diameter; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Dyes; Evolution; Foundations; Fractals; Gel; Glean; Goals; Heterogeneity; Histologic; Histology; Hour; Image; Individual; Joints; Length; MRI Scans; Magnetic Resonance Imaging; Maps; Mathematics; Measurement; Measures; Methodology; Methods; Microscopic; Modeling; Monitor; Multicenter Studies; Multicenter Trials; Neurosciences; Neurosciences Research; Normal Statistical Distribution; Outcome; Pathologic; Pathology; Pathway interactions; Physiologic pulse; Population; Porosity; Process; Property; Protons; Radial; Relaxation; Resolution; Scheme; Shapes; Signal Transduction; Source; Specimen; Stains; Statistical Distributions; Stroke; Structure; Testing; Time; Tissues; Translating; Translations; Trauma; Uncertainty; Visualization; Water; Work; bench to bedside; brain tissue; cancer diagnosis; cancer imaging; cell water; clinical diagnostics; clinically relevant; data reduction; diffusion anisotropy; experimental study; extracellular; gray matter; high dimensionality; imaging biomarker; imaging modality; improved; in vivo; innovation; interest; invention; mathematical model; migration; mild traumatic brain injury; millimeter; neural; novel; novel strategies; physical model; quantitative imaging; radiologist; reconstruction; soft tissue; submicron; tractography; tumor; water diffusion; white matter

We continue to invent and develop novel quantitative Magnetic Resonance Imaging (MRI) methods and translate these 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", which is abundant in tissue, 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 "Streamline" Tractography are 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. From it we calculate scalar parameters that are intrinsic to the tissues without the need of exogenous contrast agents or dyes; these stains are obtained by following different characteristics of endogenous water protons 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 in the brain for the first time in vivo. The development direction-encoded color (DEC) maps 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. This distribution has recently been repurposed in many data science applications. 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 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 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 as in gray matter, which are macroscopically isotropic (like a homogeneous gel) but filamentous at the microscopic length scale. 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 to advance 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 new 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, such as diffusion kurtosis imaging (DKI). 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 basis of Hermite function to represent the average propagator to compresses the 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 long-term initiative in our group has been the invention and development of several efficient and accurate 2D-and higher-dimensional MRI relaxometry/diffusometry/exchange methodologies. These include ways to measure joint distributions, i.e., correlations among diffusivity, T1 and T2, including exchange between and among them. From the standpoint of microstructure imaging, these approaches provide increasing evidence of the existence of multiple distinct water compartments within neural tissue which have been previously undetectable using conventional MRI methods. To help migrate these from bench to bedside, we have developed means to dramatically reduce the amount of acquired MR data required to estimate these multidimensional distributions. Our first approach was to use compressed sensing. Then we incorporated a priori information about these distributions to vastly reduce the data required (e.g., with Marginal Distribution Constrained Optimization (MADCO)). More recently, Cai et al. showed how to measure diffusion exchange spectra with only four measurements! Collectively, these novel methods 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 established using 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 (DTD) MRI to characterize the heterogeneity of diffusive transport within individual voxels.

我们继续发明和开发新的定量磁共振成像（MRI）方法，并将这些方法从“实验室到床边”。具体来说，我们探索了新的方法来评估体内和非侵入性的组织结构和结构，主要是通过“跟随水”，这是丰富的组织，目的是实现在神经科学和生物医学研究界的应用，并转化这些新方法来改善临床结果。