Washington University

Sheng-Kwei Song

Yong Wang

Qing Wang

Tsen-Hsuan Lin

Peng Sun

William M Spees

Chunyu Song

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 Tspectroscopy 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.

Imaging nerve function and pathologies using diffusion basis spectrum imaging

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 0.3 and the non-restricted isotopic diffusion portion includes a fraction of the isotropic diffusion portion with an apparent diffusion coefficient of at least 0.3.

Diagnosis of central nervous system white matter pathology using diffusion MRI

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. The diffusivity of isotropic diffusion component and the volume ratios of each fiber component and isotropic components are calculated.

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Manchester, MO, United States of America

Sheng-Kwei Song