Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for filtering a set of tracts that are predicted to be included in a selected neuro-network. In one aspect, a method comprises: receiving selection data selecting a network of a brain, processing magnetic resonance image data of the brain to identify a set of tracts that are predicted to be included in the selected network, determining a blocking surface for the selected network, comprising: obtaining a set of parcellations for the selected network and determining, based on a respective position in the brain of each parcellation in the set of parcellations, a set of parameters defining the blocking surface, and generating filtered tract data by filtering the set of tracts that are predicted to be included in the selected network to remove tracts that intersect the blocking surface.

The invention provides for a medical imaging system (100, 400), comprising: The execution of the machine executable instructions (112) causes a processor (104) to: receive (200) a set of input white matter fiber tracts (118): receive (202) the label from a discriminator neural network (116) in response to inputting the set of input w hue matter fiber tracts, generate (204) an optimized feature vector (122) using the set of input white matter fiber tracts and a generator neural network ((114) if the label indicates anatomically incorrect; receive (206) the set of generated white matter fiber tracts from the generator neural network in response to inputting the optimized feature vector, and construct (208) a false positive subset (126) of the set of input white matter fiber tracts using the generated set of white matter fiber tracts.

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.

Disclosed is a computer-implemented method of determining one or more position and/or orientation parameters of an anatomical structure of a body portion. The anatomical structure has a longitudinal shape defining a longitudinal axis. The method includes generating and/or reading, by a data processing system, volumetric data of at least a portion of a subject. The method further includes generating and/or reading, by the data processing system, a deformable template which provides an estimate for a location of the longitudinal axis in the portion of the subject. The method further includes matching, by the data processing system, the deformable template to the volumetric data, thereby obtaining a matched template. The matching comprises using one or more internal energy functions and one or more external energy functions for optimizing an objective function. The method further includes determining, by the data processing system, the at least one position and/or orientation parameter based on the matched template.

A computing device: compares an anatomical magnetic resonance (MR) image of a patient region and reference anatomical data associated with the region to determine a first transform of a bore anatomical coordinate space of the anatomical MR image to a patient anatomical coordinate space associated with the patient; determines, from the first transform, a second transform of a bore DWMR coordinate space of a DWMR image to a patient DWMR coordinate space associated with the patient, the anatomical and the DWMR images being in respective bore coordinate spaces associated with a bore of an MR device which acquired the anatomical and the DWMR images; transforms, using the second transform, the DWMR image to the patient DWMR coordinate space; and controls a display screen to render the DWMR image, as transformed, according to visual attributes associated with the patient DWMR coordinate space.

A method and a product for AI recognizing of embolism based on VRDS 4D medical image, the method is applied to a medical imaging apparatus, and the method includes the following steps: determining a bitmap (BMP) data source according to a plurality of scanned images of a target site of a target user, wherein the target site includes an embolism formed on a wall of a target blood vessel; generating target medical image data according to the BMP data source; performing 4D medical imaging according to the target medical image data and determining a feature attribute of the embolism according to an imaging result, wherein the feature attribute includes at least one of the following: density, crawling direction, correspondence with a site of cancer focus and edge characteristics; and determining a type of the embolism according to the features and outputting the type.

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 method to increase the reliability and clinical utility of diffusion tensor imaging (DTI) of traumatic brain injury (TBI) in single subjects and a semi-automated method of identifying and quantifying small hemorrhages using susceptibility-weighted images (SWI) of single subjects include storing an image template formed from control subjects, storing a brain image of the single subject, correcting for image acquisition differences of the control subjects and single subject, and performing regional analysis of the brain image of the single subject. The method may include analysis of fractional anisotropy values that are age-corrected between the control subjects and the single subject before performing voxel-based analysis (VBA), and a hybrid VBA and tract-based spatial statistical (TBSS) analysis with the VBA and TBSS results combined using a statistical calculation. The resulting combined DTI image may be further combined with an SWI image, FLAIR image, and/or T1 image of the single subject.

Disclosed is a computer-implemented method of determining one or more position and/or orientation parameters of an anatomical structure of a body portion. The anatomical structure has a longitudinal shape defining a longitudinal axis. The method includes generating and/or reading, by a data processing system, volumetric data of at least a portion of a subject. The method further includes generating and/or reading, by the data processing system, a deformable template which provides an estimate for a location of the longitudinal axis in the portion of the subject. The method further includes matching, by the data processing system, the deformable template to the volumetric data, thereby obtaining a matched template. The matching comprises using one or more internal energy functions and one or more external energy functions for optimizing an objective function. The method further includes determining, by the data processing system, the at least one position and/or orientation parameter based on the matched template.

A method and system for image segmentation systems and related methods of automatically segmenting cardiac MRI images using deep learning methods. One example method includes inputting MRI volume data from a MRI scanner, segmenting the MRI volume data with a whole volume segmentation analysis module, assembling the segmented MRI volume data into a 3D volume assembly with a 3D volume assembly module, determining the 3D volume assembly for anatomic plausibility with an anatomic plausibility analysis module, and outputting a final segmented 3D volume assembly.