Abstract: Disclosed are methods to determine a homeostatic steady-state of a biological entity. Also disclosed are methods to quantify an exchange rate between at least two compartments in a biological system, to use Nuclear Magnetic Resonance (NMR) to characterize physiological water transport, and methods for non-invasively measuring transmembrane exchange rates of endogenous water in a biological system under steady-state or non-steady-state conditions in near-real time.

Abstract: Magnetic resonance and ultrasound methods can produce estimates of full rank-4 elasticity tensors (E-tensors) using suitable constraints. E-tensor estimates can be based on E-tensor symmetry conditions and a suitable E-tensor selected from among a set of E-tensors calculated using different symmetry constraints. Displacement fields used in E-tensor calculations can be noise reduced using compatibility conditions. With the selected E-tensor, various stains that are rotation invariant can be computed. In one example. an E-tensor for an in vivo brain is computed using the mechanical disturbance associated with cardiac pulsations. The selected E-tensor and associated stains. physiological disorders such as Alzheimer's disease and traumatic brain injury (TBI) and even neural activation may be more readily detected than with conventional methods that do not use the full E-tensor.

Abstract: Multi-dimensional spectra associated with a specimen are reconstructed using lower dimensional spectra as constraints. For example, a two-dimensional spectrum associated with diffusivity and spin-lattice relaxation time is obtained using one-dimensional spectra associated with diffusivity and spin-lattice relaxation time, respectively, as constraints. Data for a full two dimensional spectrum are not acquired, leading to significantly reduced data acquisition times.

Abstract: Isotropic generalized diffusion tensor imaging methods and apparatus are configured to obtain signal attenuations using selected sets of applied magnetic field gradient directions whose averages produce mean apparent diffusion constants (mADCs) over a wide range of b-values, associated with higher order diffusion tensors (HOT). These sets are selected based on analytical descriptions of isotropic HOTs and the associated averaged signal attenuations are combined to produce mADCs, or probability density functions of intravoxel mADC distributions. Estimates of biologically-specific rotation-invariant parameters for quantifying tissue water mobilities or other tissue characteristics can be obtained such as Traces of HOTs associated with diffusion and mean t-kurtosis.

Abstract: Multidimensional MRI-based methods permit identification and categorization of brain specimens to identify sub-voxel tissue components that are specific to traumatic axon injury or other lesions. Lower dimensional MR spectral data is acquired and processed to provide multidimensional MR data of higher dimensions. One or more spectral ranges are selected that define signatures for brain injury and evaluation of the multidimensional MR data in these ranges is used to locate voxels associated with brain injury. For example, partial one dimensional data sets such as T1, T2, and mean diffusion coefficient (MD) data sets can be combined to provide two dimensional data sets such as T1-T2, MD-T2, and MD-T1 data sets. Using the spectral signatures, a specimen image can be produced showing areas of lesser or greater injury.

Abstract: Diffusion sensitizing gradient pulse pairs are prescribed in a manner to mitigate effects of concomitant gradient artifacts. Measured MR signals generated by applying a plurality of diffusion sensitizing gradient matrices are obtained and processed to determine a second order mean diffusion tensor and a fourth order covariance tensor. Quantities derived from these tensors are measured and mapped within an imaging volume which describe features of diffusion anisotropy and heterogeneity within each imaging voxel.

Abstract: Multi-dimensional spectra associated with a specimen are reconstructed using lower dimensional spectra as constraints. For example, a two-dimensional spectrum associated with diffusivity and spin-lattice relaxation time is obtained using one-dimensional spectra associated with diffusivity and spin-lattice relaxation time, respectively, as constraints. Data for a full two dimensional spectrum are not acquired, leading to significantly reduced data acquisition times.

Abstract: Multi-dimensional spectra associated with a specimen are reconstructed using lower dimensional spectra as constraints. For example, a two-dimensional spectrum associated with diffusivity and spin-lattice relaxation time is obtained using one-dimensional spectra associated with diffusivity and spin-lattice relaxation time, respectively, as constraints. Data for a full two dimensional spectrum are not acquired, leading to significantly reduced data acquisition times.

Abstract: Magnetic resonance methods comprise tractographically establishing a path along a structure in a specimen and finding a distribution of structure radii or cross-sectional areas along the path. Based on the distribution and the path, end-to-end functional characteristics of the structure are estimated. For example, nerve transit times or distributions of transit times can be estimated for a plurality of nervous system locations such as Brodmann areas. Comparison of estimated transit times or distributions thereof between reference values or other values from the same structure can be used to assess specimen health.

Abstract: Magnetic resonance methods comprise tractographically establishing a path along a structure in a specimen and finding a distribution of structure radii or cross-sectional areas along the path. Based on the distribution and the path, end-to-end functional characteristics of the structure are estimated. For example, nerve transit times or distributions of transit times can be estimated for a plurality of nervous system locations such as Brodmann areas. Comparison of estimated transit times or distributions thereof between reference values or other values from the same structure can be used to assess specimen health.

Abstract: Described herein are exemplary methods for estimating a nonparametric joint radius-length (R-L) distribution of an ensemble of porous elements represented generally by finite cylinders. Some described methods comprise estimating an eccentricity distribution of a group of anisotropic porous elements. For example, disclosed methods can be applied to estimate a nonparametric joint R-L distribution of injured axons in nervous tissue, muscle tissue, plant elements, or other porous materials. Employing a novel three dimensional (3-D) double pulsed-field gradient (d-PFG) magnetic resonance (MR) acquisition scheme, both the marginal radius and length distributions of a population of generally cylindrical porous elements can be obtained. The marginal radius and length distributions can then b e used to constrain and stabilize the estimate of the joint radius-length distribution. Using the marginal distributions as constraints allows the joint R-L distribution to be reconstructed from an underdetermined system (i.e.

Abstract: Isotropic generalized diffusion tensor imaging methods and apparatus are configured to obtain signal attenuations using selected sets of applied magnetic field gradient directions whose averages produce mean apparent diffusion constants (mADCs) over a wide range of b-values, associated with higher order diffusion tensors (HOT). These sets are selected based on analytical descriptions of isotropic HOTs and the associated averaged signal attenuations are combined to produce mADCs, or probability density functions of intravoxel mADC distributions. Estimates of biologically-specific rotation-invariant parameters for quantifying tissue water mobilities or other tissue characteristics can be obtained such as Traces of HOTs associated with diffusion and mean t-kurtosis.

Abstract: An approach is presented to recontruct image data for an object using a partial set of magnetic resonance (MR) measurements. A subset of data points in a data space representing an object are selected (e.g. through random sampling) for MR data acquisition. Partial MR data corresponding to the subset of data points is received and used for image reconstruction. The overall speed of image reconstruction can be reduced dramatically by relying on acquisition of data for the subset of data points rather than for all data points in the data space representing the object. Compressive sensing type arguments are used to fill in missing measurements, using a priori knowledge of the structure of the data. A compressed data matrix can be recovered from measurements that form a tight frame. It can be established that these measurements satisfy the restricted isometry property (RIP). The zeroth-order regularization minimization problem can then be solved, for example, using a 2D ILT approach.

Abstract: An approach is presented to reconstruct image data for an object using a partial set of magnetic resonance (MR) measurements. A subset of data points in a data space representing an object are selected (e.g. through random sampling) for MR data acquisition. Partial MR data corresponding to the subset of data points is received and used for image reconstruction. The overall speed of image reconstruction can be reduced dramatically by relying on acquisition of data for the subset of data points rather than for all data points in the data space representing the object. Compressive sensing type arguments are used to fill in missing measurements, using a priori knowledge of the structure of the data. A compressed data matrix can be recovered from measurements that form a tight frame. It can be established that these measurements satisfy the restricted isometry property (RIP). The zeroth-order regularization minimization problem can then be solved, for example, using a 2D ILT approach.

Abstract: An approach is presented to recontruct image data for an object using a partial set of magnetic resonance (MR) measurements. A subset of data points in a data space representing an object are selected (e.g. through random sampling) for MR data acquisition. Partial MR data corresponding to the subset of data points is received and used for image reconstruction. The overall speed of image reconstruction can be reduced dramatically by relying on acquisition of data for the subset of data points rather than for all data points in the data space representing the object. Compressive sensing type arguments are used to fill in missing measurements, using a priori knowledge of the structure of the data. A compressed data matrix can be recovered from measurements that form a tight frame. It can be established that these measurements satisfy the restricted isometry property (RIP). The zeroth-order regularization minimization problem can then be solved, for example, using a 2D ILT approach.

Abstract: Multi-dimensional spectra associated with a specimen are reconstructed using lower dimensional spectra as constraints. For example, a two-dimensional spectrum associated with diffusivity and spin-lattice relaxation time is obtained using one-dimensional spectra associated with diffusivity and spin-lattice relaxation time, respectively, as constraints. Data for a full two dimensional spectrum are not acquired, leading to significantly reduced data acquisition times.

Abstract: Described herein are exemplary methods for estimating a nonparametric joint radius-length (R-L) distribution of an ensemble of porous elements represented generally by finite cylinders. Some described methods comprise estimating an eccentricity distribution of a group of anisotropic porous elements. For example, disclosed methods can be applied to estimate a nonparametric joint R-L distribution of injured axons in nervous tissue, muscle tissue, plant elements, or other porous materials. Employing a novel three dimensional (3-D) double pulsed-field gradient (d-PFG) magnetic resonance (MR) acquisition scheme, both the marginal radius and length distributions of a population of generally cylindrical porous elements can be obtained. The marginal radius and length distributions can then b e used to constrain and stabilize the estimate of the joint radius-length distribution. Using the marginal distributions as constraints allows the joint R-L distribution to be reconstructed from an underdetermined system (i.e.

Abstract: A phantom calibration body (12) for calibrating diffusion MRI device (16) that mimics a material such as a mammalian tissue is disclosed. The phantom calibration body (12) includes a homogeneous aqueous solution (30) that contains a mixture of low molecular-weight and high molecular-weight polymers housed in a container (14) that is placed in the diffusion MRI device (16) for obtaining one or more diffusion MRI images of the phantom calibration body (12). A measure of diffusivity is calculated for each of the one or more diffusion MRI images in order to calibrate the diffusion MRI device. Methods of using the phantom calibration body (12) to calibrate diffusion MRI device (16) are also disclosed.

Abstract: A phantom calibration body (12) for calibrating diffusion MRI device (16) that mimics a material such as a mammalian tissue is disclosed. The phantom calibration body (12) includes a homogeneous aqueous solution (30) that contains a mixture of low molecular-weight and high molecular-weight polymers housed in a container (14) that is placed in the diffusion MRI device (16) for obtaining one or more diffusion MRI images of the phantom calibration body (12). A measure of diffusivity is calculated for each of the one or more diffusion MRI images in order to calibrate the diffusion MRI device. Methods of using the phantom calibration body (12) to calibrate diffusion MRI device (16) are also disclosed.

Abstract: An approach is presented to recontruct image data for an object using a partial set of magnetic resonance (MR) measurements. A subset of data points in a data space representing an object are selected (e.g. through random sampling) for MR data acquisition. Partial MR data corresponding to the subset of data points is received and used for image reconstruction. The overall speed of image reconstruction can be reduced dramatically by relying on acquisition of data for the subset of data points rather than for all data points in the data space representing the object. Compressive sensing type arguments are used to fill in missing measurements, using a priori knowledge of the structure of the data. A compressed data matrix can be recovered from measurements that form a tight frame. It can be established that these measurements satisfy the restricted isometry property (RIP). The zeroth-order regularization minimization problem can then be solved, for example, using a 2D ILT approach.