Abstract: Repetitive electrical activity produces microstructural alteration in myelinated axons. These transient microstructural changes can be non-invasively visualized via two different magnetic-resonance-based approaches: diffusion fMRI and dynamic T2 spectroscopy in the ex vivo perfused bullfrog sciatic nerves. Non-invasive diffusion fMRI, based on standard diffusion tensor imaging (DTI), clearly localized the sites of axonal conduction blockage as might be encountered in neurotrauma or other lesion types. Diffusion fMRI response was graded in proportion to the total number of electrical impulses carried through a given locus. Diffusion basis spectrum imaging (DBSI) method revealed a reversible shift of tissue water into a restricted isotropic diffusion signal component, consistent with sub-myelinic vacuole formation.

Abstract: A method of performing diffusion basis spectrum imaging (DBSI) within a tissue of a patient using diffusion magnetic resonance data acquired from a portion of the tissue is disclosed. The diffusion magnetic resonance data includes a plurality of diffusion MR signals associated with one voxel and the one voxel represents an image of the portion of the tissue. The method includes computing an isotropic diffusion portion of the diffusion magnetic resonance data representing isotropic diffusion within the one voxel and dividing the isotropic diffusion portion, which includes a fraction of the diffusion magnetic resonance data representing isotropic diffusion, into a restricted isotropic diffusion portion and a non-restricted isotopic diffusion portion. The restricted isotropic diffusion portion includes a fraction of the isotropic diffusion portion with an apparent diffusion coefficient of less than 0.

Abstract: Repetitive electrical activity produces microstructural alteration in myelinated axons. These transient microstructural changes can be non-invasively visualized via two different magnetic-resonance-based approaches: diffusion fMRI and dynamic T2 spectroscopy in the ex vivo perfused bullfrog sciatic nerves. Non-invasive diffusion fMRI, based on standard diffusion tensor imaging (DTI), clearly localized the sites of axonal conduction blockage as might be encountered in neurotrauma or other lesion types. Diffusion fMRI response was graded in proportion to the total number of electrical impulses carried through a given locus. Diffusion basis spectrum imaging (DBSI) method revealed a reversible shift of tissue water into a restricted isotropic diffusion signal component, consistent with sub-myelinic vacuole formation.

Abstract: D-Histo, a non-invasive diagnostic method, renovated from diffusion basis spectrum imaging (DBSI) is provided for quantitatively detecting and distinguishing inflammation from solid tumors, heart and nerve injury. For example, the D-Histo methods disclosed herein provide an accurate diagnosis of prostate cancer, distinguishing it from prostatitis and BPH that missed by currently available methods of diagnosing prostate cancer (multiparameter MRI, needle biopsy). The disclosed D-Histo method also provides metrics to reflect reversible vs. irreversible damages in heart and central/peripheral nerves. For central and peripheral nerves, D-Histo also provides metrics to assess nerve functionality. The at least one D-Histo biomarker obtained using diffusion weighted MRI has excellent test-retest stability, high sensitivity to disease progression and close correlation with currently available techniques.

Abstract: A method of performing diffusion basis spectrum imaging (DBSI) within a tissue of a patient using diffusion magnetic resonance data acquired from a portion of the tissue is disclosed. The diffusion magnetic resonance data includes a plurality of diffusion MR signals associated with one voxel and the one voxel represents an image of the portion of the tissue. The method includes computing an isotropic diffusion portion of the diffusion magnetic resonance data representing isotropic diffusion within the one voxel and dividing the isotropic diffusion portion, which includes a fraction of the diffusion magnetic resonance data representing isotropic diffusion, into a restricted isotropic diffusion portion and a non-restricted isotopic diffusion portion. The restricted isotropic diffusion portion includes a fraction of the isotropic diffusion portion with an apparent diffusion coefficient of less than 0.

Abstract: Determining diffusivity of multiple diffusion components within a tissue using diffusion magnetic resonance data representing a volume of the tissue. A plurality of candidate fibers having a direction is defined within the volume. A possibility coefficient is calculated by a processor for each candidate fiber of the plurality of candidate fibers based on the magnetic resonance data and the direction of the candidate fiber. The possibility coefficient represents a likelihood that the candidate fiber exists in the volume. Candidate fibers associated with a possibility coefficient greater than a threshold value are selected by the processor to create one or more probable fibers. For each probable fiber of the one or more probable fibers, an axial diffusivity indicating a diffusion of water in the direction of the probable fiber and a radial diffusivity indicating a diffusion of water perpendicular to the direction of the probable fiber are calculated by the processor.

Abstract: Determining diffusivity of multiple diffusion components within a tissue using diffusion magnetic resonance data representing a volume of the tissue. A plurality of candidate fibers having a direction is defined within the volume. A possibility coefficient is calculated by a processor for each candidate fiber of the plurality of candidate fibers based on the magnetic resonance data and the direction of the candidate fiber. The possibility coefficient represents a likelihood that the candidate fiber exists in the volume. Candidate fibers associated with a possibility coefficient greater than a threshold value are selected by the processor to create one or more probable fibers. For each probable fiber of the one or more probable fibers, an axial diffusivity indicating a diffusion of water in the direction of the probable fiber and a radial diffusivity indicating a diffusion of water perpendicular to the direction of the probable fiber are calculated by the processor.