4 results about "Fiber tract" patented technology

A computer-implemented method for performing diffusion tractography of a subject's brain, includes generating, by a computer system that includes at least one processor in communication with at least one memory system, for each canonical fiber tract bundle in a canonical fiber tract model, subject specific regions of interest (ROIs), detecting, by the computer system, a canonical set of fiber tracts in magnetic resonance (MR) data of the subject's brain based on the canonical fiber tract model and the subject specific ROIs, and generating, by the computing system, a subject-specific brain map comprising the canonical set of fiber tracts detected in the MR data of the subject's brain.

The disclosed system provides an integrated neuroimaging analytics platform for comprehensive preoperative neurosurgical planning, intraoperative neurosurgical guidance and post-operative assessment by processing multimodal MRI (structural, diffusion, functional, and angiography) and CT data. The platform performs detailed anatomical characterization by delineating tumor subregions (peritumoral edema, enhancing tumor, and necrotic core), segmenting brain tissues (gray matter, white matter, and CSF), and executing lobe, cortical / subcortical parcellation. Advanced 3D rendering visualizes tumors alongside critical white matter fibre tracts derived from diffusion MRI, while MRA data is used to segment cerebrovascular structures, and functional MRI analysis identifies eloquent cortices associated with motor, speech, and visual functions. All results are integrated within a user-friendly GUI featuring advanced multiplanar slicing and a smart brush for interactive mask editing, complemented by a speech-to-text engine for streamlined analytical reporting. This comprehensive approach facilitates precise and efficient surgical planning, thereby enhancing patient safety and improving clinical outcomes.

Exemplary system, method and computer arrangement for determining rotational invariants, fiber orientations, and scalar parameters of fiber tracts (e.g., compartment fractions, which can relate to intra / extra-cellular space volumes; compartment diffusivities; relaxation rates; exchange rates between compartments; characteristics of structural disorder such as axonal beading) from a general diffusion MRI acquisition is described. For example, gradient directions may not necessarily be arranged in so-called shells, and an acquisition may vary spatially. Furthermore, each acquisition can be undersampled in the k-space. A procedure can also be included for receiving information related to the at least one image. Another procedure can be provided for decoupling tissue and protocol parameters based on a singular value decomposition. A further procedure can be provided for grouping singular vectors into multiplets based on symmetries. Still further procedures can be provided for forming rotational invariants and / or for a parameter estimation.

This disclosure relates to an artificial intelligence-driven method, apparatus, device, and medium for simulating and generating white matter fiber tracts based on CT images. The method includes: acquiring a training dataset, which includes CT images of patients with cerebral hemorrhage and diffusion tensor imaging data paired with the CT images; establishing a fiber tract model and generating CST anatomical labels in the diffusion tensor imaging space; mapping the CST anatomical labels in the diffusion tensor imaging space to the corresponding CT space using a linear registration algorithm; annotating the CT images with CST fiber tracking masks based on the CST anatomical labels in the CT space; training a CST prediction model based on the training dataset; and inputting the CT image to be predicted into the CST prediction model to generate a CST prediction mask for the CT image to be predicted. According to the technical solution of this disclosure, damage assessment of key white matter tracts in patients with cerebral hemorrhage can be achieved under resource-constrained conditions.