A system and method for optimized diffusion-weighted imaging is provided. In one aspect, the method includes providing a plurality of constraints for imaging a target at a selected diffusion weighting, and applying an optimization framework to generate an optimized diffusion encoding gradient waveform satisfying the plurality of constraints. The method also includes performing, using the MRI system, a pulse sequence comprising the optimized diffusion encoding gradient waveform to generate diffusion-weighted data, and generating at least one image of the target using the diffusion-weighted data.

A system of generating data from diffusion-weighted images for pre-processing and a method thereof are disclosed. In the system, a processing parameter set including diffusion information is acquired; after a raw diffusion-weighted image including data images and image information is acquired, the image information is interpreted to set image processing data of the raw diffusion-weighted image, and non-deformation correction and deformation correction are performed on the raw diffusion-weighted image to generate a pre-processed diffusion-weighted image based on the processing parameter set and the image processing data. Therefore, the image processing data can be automatically set based on the raw diffusion-weighted image, to achieve the effect of lowering difficulty for analyzing DWI and saving setup time of image processing data.

A mechanism is provided in a data processing system for automatic determination of b-value difference from diffusion-weighted (DW) images. The mechanism receives a series of images wherein a first image has a first b-value and a second image has an unknown b-value. The mechanism applies a generative adversarial network (GAN) model to estimate a difference between b-values in the series of images. The mechanism determines a b-value for the second image based on the first b-value and the estimated difference between b-values.

An imaging method may include obtaining imaging data associated with a region of interest (ROI) of an object. The imaging data may correspond to a plurality of time-series images of the ROI. The imaging method may also include determining, based on the imaging data, a data set including a spatial basis and one or more temporal bases. The spatial basis may include spatial information of the imaging data. The one or more temporal bases may include temporal information of the imaging data. The imaging method may also include storing, in a storage medium, the spatial basis and the one or more temporal bases.

A method (400) including: determining (702) a registration function [705, Niirf(T1)] for the particular brain in a coordinate space, determining (706) a registered atlas [708, Ard(T1)] from the registration function and an HCP-MMP1 Atlas (102) containing a standard parcellation scheme, performing (310, 619) diffusion tractography to determine a set [621, DTIp(DTI)] of brain tractography images of the particular brain, for a voxel in a particular parcellation in the registered atlas, determining (1105, 1120) voxel level tractography vectors [1123, Vje, Vjn] showing connectivity of the voxel with voxels in other parcellations, classifying (1124) the voxel based on the probability of the voxel being part of the particular parcellation, and repeating (413) the determining of the voxel level tractography vectors and the classifying of the voxels for parcellations of the HCP-MMP1 Atlas to form a personalised brain atlas [1131, PBs Atlas] containing an adjusted parcellation scheme reflecting the particular brain (Bbp).

There is described herein a method for recovering missing information in diffusion magnetic resonance imaging (dMRI) data. The data are modeled according to the theory of moving frames and regions where frame information is missing are reconstructed by performing diffusions into the regions. Local orthogonal frames computed along the boundary of the regions are rotated into the regions. Connection parameters are estimated at each new data point obtained by a preceding rotation, for application to a subsequent rotation.

In white matter tractography from magnetic resonance imaging, a mathematical representation of diffusion (e.g., fiber orientation distributions) is first estimated from the diffusion MR data. Fiber tracing is performed via deterministic or probabilistic tractography where the tract maps and brain regions from multiple atlases and/or templates can be used for seeding and/or as spatial constraints. Field map correction and/or denoising may improve the diffusion weighted imaging data used in tractography.

A method and system for positioning a target in a brain region are provided. The method includes: obtaining datasets of N persons at a first time point and a second time point after stroke; constructing a first lesion mapping functional network based on each resting-state functional magnetic resonance imaging image in a first stroke dataset; constructing an acute phase cognitive-lesion mapping functional network; constructing a chronic phase cognitive-lesion mapping functional network; comparing the acute phase cognitive-lesion mapping functional network with the chronic phase cognitive-lesion mapping functional network to obtain a key improvement network; calculating a whole-brain functional connectivity network with each voxel as a seed point, and performing spatial correlation calculation on the whole-brain functional connectivity network and the key improvement network to obtain a spatial correlation network; and determining a therapeutic target of the functional image to be positioned.

New and innovative systems and methods for registration degradation correction for surgical procedures are disclosed. An example system includes a surgical marking pen including a first trackable target and a registration plate including a second trackable target. The system also includes a navigation camera and a processor configured to perform a pen registration that determines a transformation between a tip of the surgical marking pen and the first trackable target when the tip of the surgical marking pen is placed on the registration plate. The pen registration enables the processor to record virtual marks at locations of the pen tip that correspond to physical marks drawn by the pen. Locations of the virtual marks are later compared to images of the physical marks to correct any registration degradation by moving a surgical camera or robotic arm connected to the surgical camera.

A method is to be implemented by a computing device and includes steps of: a) identifying, according to a magnetic resonance imaging (MRI) image, brain image regions each of which contains a respective portion of diffusion index values of at least one diffusion index, which results from image processing performed on said at least one MRI image; b) for each of the brain image regions, calculating a characteristic parameter based on the respective portion of the diffusion index values; and c) diagnosing the brain examined with one of predetermined categories of the neurological disorder by performing classification on a combination of the characteristic parameters via a classifier.