The present disclosure discusses systems and methods for identifying biomarkers that can help with the diagnosis, prognosis, and treatment choices of patients with neurodegenerative diseases. Diffusion based magnetic resonance imaging can often fail for patients with a neurodegenerative disease because parameters fractional anisotropy, mean diffusivity, and radial diffusivity are based on simple models that can fail in the presence of neurodegeneration, such as demyelination. The present disclosure discusses systems and methods that enhance dMRI images and enable tractography to be performed on images of a damaged nervous system. The damaged tracks identified by the present system can be used as a biomarker for the assessment of patients. In some implementations, the biomarkers are converted into clinical scales that can be used to compare patients to one another or over time.

Systems and methods are contemplated for monitoring and analyzing the glymphatic system and brain to predict, prognose, diagnose, treat, modify or improve treatment, and track progression of neurological diseases. A first and second MRI image are taken of an extracellular space in a region of interest in a patient's brain, with one image taken while the patient is awake and the other image taken while the patient is asleep. The first and second images are compared to detect changes in the extracellular space, and the comparison is used to predict, prognose, diagnose, treat, modify or improve treatment, and track progression of neurological diseases associated with the extracellular space.

A method of predicting a brain atrophy condition for an individual using the individual's predicted age difference (PAD). The method comprises acquiring at least one brain image of the individual; processing the brain image to obtain at least one feature of the brain image; generating a PAD value of the individual based on the at least one feature of the image; and determining a brain atrophy condition of the individual based on the PAD value.

Disclosed herein are methods and devices for generating novel diffusion ellipsoid maps from diffusion tensor imaging (DTI) scan data. One example method includes: (i) generating, from DTI scan data, for each voxel in a plurality of voxels in one or more slabs of a target tissue, a respective diffusion tensor; (ii) generating, for each voxel, eigenvalues and eigenvectors of the respective diffusion tensor and a respective set of diffusion parameters; (iii) partitioning the voxels into two groups, wherein voxels, whose respective set of diffusion parameters is such that each element in the set is smaller than a corresponding element in a set of thresholds, are substantially all in a first group of the two groups; and (iv) providing a graphical representation of a diffusion ellipsoid map of at least one of the one or more slabs, wherein ellipsoids, associated with voxels in the first group, are displayed differently to the other ellipsoids. The utility of the disclosed methods and devices in breast cancer detection is demonstrated.

The present invention is related to a method for correcting a diffusion image having an artifact, the method comprising: (a) providing a set of diffusion images comprising the diffusion image having the artifact; (b) calculating a first signal intensity of each image in the set of diffusion images; (c) plotting a graph of serial number of slice of the set of diffusion images versus the first signal intensity; (d) calculating a second signal intensity of the diffusion image having the artifact by performing interpolation on the graph; and (e) correcting the diffusion image having the artifact base on the second signal intensity.

An image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry selects from a plurality of diffusion-weighted images with different applied directions of a motion probing gradient magnetic field pulse or different diffusion sensitive coefficients, a diffusion-weighted image that conforms to a predetermined condition. The processing circuitry configured to register the selected diffusion-weighted image with a reference image for which the diffusion sensitive coefficient being set to a reference value to correct the selected diffusion-weighted image. The processing circuitry configured to register the corrected diffusion-weighted image with each of the other diffusion-weighted images of the plurality of diffusion-weighted images to correct each of the other diffusion-weighted images.

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.

A method of analyzing a multidimensional image tensor containing a plurality of images comprises: performing imaging scans of a subject imaging data; generating the multidimensional image tensor from the imaging data; determining a spatial basis tensor containing basis images based on the multidimensional image tensor; determining a temporal basis tensor containing basis functions for a temporal dimension based on the multidimensional image tensor; determining a core tensor that relates the spatial basis tensor to the temporal basis tensor; pre-multiplying the core tensor and the temporal basis tensor to produce a modified temporal basis tensor; storing the spatial basis tensor and the modified temporal basis tensor; and generating an image by multiplying at least (i) at least a portion of the spatial basis tensor and (ii) at least a portion of the modified temporal basis tensor.

The present invention relates to a medical data processing method for determining an orientation of nerve fibres relative to a non-physiological electric field, the method comprising the following steps which are constituted to be executed by a computer: a) acquiring patient medical image data describing a patient medical image of an anatomical body part of a patient's body, the anatomical body part including nerve tissue comprising white matter nerve fibres; b) acquiring diffusion image data describing a diffusion-enhanced image of the anatomical body part; c) acquiring atlas data describing a spatial distribution of grey value-based tissue classes in a model body part representing a model of the anatomical body part; d) determining, based on the patient image data and the diffusion image data and the atlas data, fibre orientation data describing an orientation of the white matter nerve fibres; e) acquiring electric field orientation data describing an orientation of the non-physiological electric field which is an electric field simulated around a simulated electrode virtually placed in a predetermined spatial relationship relative to the anatomical body part; f) determining, based on the fibre orientation data and the electric field orientation data, relative orientation data describing a relative orientation between the orientation of the white matter nerve fibres and the orientation of the electric field.

1. A medical data processing method of determining information describing the probable position of a neural fibre in a patient's brain, the method comprising the following steps which are constituted to be executed by a computer: a) acquiring patient-specific medical image data describing the brain of the patient; b) acquiring atlas data defining an image-based model of a human brain; c) determining, based on the patient-specific medical image data and the atlas data, seed region data describing seed regions (A, B) in the patient-specific medical image data in which the ends of neural fibres of the patient's brain may be located; d) determining, based on the patient-specific medical image data and the seed region data, neural fibre tract data describing a plurality of potential tracts (T1, T2, T3) which a specific neural fibre may take through the patient's brain; e) determining, based on the atlas data and the neural fibre tract data, a figure of merit for each one of the potential tracts (T1, T2, T3).