Provided are a method and device for automated brain white matter fiber tract segmentation combined with anatomical priors. The method includes: obtaining whole-brain fiber point coordinates and structural T1-weighted magnetic resonance images, determining superficial white matter fibers and deep white matter fibers based on the whole-brain fiber point coordinates, and generating an anatomical brain region division map based on the structural T1-weighted magnetic resonance images; determining an individual-level anatomical feature descriptor of each fiber based on the superficial white matter fibers, the deep white matter fibers and the anatomical brain region division map, and respectively determining a cluster-level anatomical feature descriptor corresponding to each fiber; and inputting the whole-brain fiber point coordinates, the individual-level anatomical feature descriptors and the cluster-level anatomical feature descriptors into a trained fiber tract segmentation model, and obtaining classification results of fiber tracts.

The present solution can segment tracts by performing two-pass tractography. The system can first perform deterministic tractography and then probabilistic tractography. The system can use the result from the deterministic tractography to update and refine initial identified regions of interest. The refined regions of interest can be used to filter and select streamlines identified through the probabilistic tractography.

Methods and systems for generating and using a multi-modal normative model of a brain are described. The method for generating the multi-modal normative model comprises receiving functional magnetic resonance imaging (fMRI) data and diffusion MRI (dMRI) data for each of a plurality of human subjects, generating, based on the fMRI data, functional connectivity data for each of the plurality of human subjects, generating, based on the dMRI data, structural connectivity data for each of the plurality of human subjects, determining, based on the structural connectivity data and/or the functional connectivity data, at least one brain network connectivity measure associated with each of a plurality of brain regions, and generating a multi-modal normative model that includes the at least one brain network connectivity measure associated with each of the plurality of brain regions.

A method of predicting an effect of ischemic stroke on an individual's cognitive status comprises (1) acquiring at least one medical brain image of an individual's brain after the ischemic stroke of the individual; (2) processing the medical brain image to obtain at least one feature of the image; (3) generating a gray matter brain age (GMBA) value of the individual based on the at least one feature of the image; and (4) predicting an effect of the ischemic stroke on the individual's post-stroke cognitive status (PSCI) using the GMBA value.

The present solution can segment tracts by performing two-pass tractography. The system can first perform deterministic tractography and then probabilistic tractography. The system can use the result from the deterministic tractography to update and refine initial identified regions of interest. The refined regions of interest can be used to filter and select streamlines identified through the probabilistic tractography.

A computer system that computes second tractography results is described. This computer may include: a computation device (such as a processor, a graphics processing unit or GPU, etc.) that executes program instructions; and memory that stores the program instructions. During operation, the computer system receives information specifying tractography results that specify a set of neurological fibers. Then, the computer system computes, using a predetermined (e.g., pretrained) autoencoder neural network, the second tractography results that specify a second set of neurological fibers based at least in part on the tractography results and information associated with a neurological anatomical region. For example, a subset of the set of neurological fibers may be anatomically implausible and the second set of fibers may exclude the subset. Note that the predetermined autoencoder neural network may be trained using an unsupervised-learning technique.

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.

A computer system that performs pairwise harmonization is described. During operation, the computer system may receive information specifying magnetic-resonance diffusion measurements, where the magnetic-resonance diffusion measurements are associated with at least an individual and a first site. Then, the computer system may perform the pairwise harmonizing of the magnetic-resonance diffusion measurements based at least in part on target magnetic-resonance diffusion measurements, where the target magnetic-resonance diffusion measurements are associated with a population and one or more target sites, and the magnetic-resonance diffusion measurements and the target magnetic-resonance diffusion measurements may be acquired using different magnetic-resonance scanners. Next, the computer system may perform quality control on the pairwise harmonized magnetic-resonance diffusion measurements, where the quality control may include comparing the pairwise harmonized magnetic-resonance diffusion measurements associated with the first site and the one or more target sites.

The invention relates to a system and method for quantifying a novel biomarker of the tissue activity of a human or animal organ. By way of preferred application, such a biomarker describes the diffusivity of biological fluids in living tissues in the form of a novel indicator of the diffusion of water molecules in living tissues on the basis of diffusion data resulting from the acquisition of a sequence of images of one or more parts of the body of an animal or human patient. Particularly resistant and stable with respect to noise present in the medical imaging signals from which the experimental data stem, the novel biomarker is relevant in a large number of applications including, inexhaustively, the analysis and/or monitoring of cancers, or the assessment of strokes.