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.

Disclosed herein is a method for diagnosing a neurological disorder based on at least one magnetic resonance imaging (MRI) image. The method includes identifying brain image regions that contain a respective portion of diffusion index values of at least one diffusion index. For each of the brain image regions, a characteristic parameter based on the respective portion of the diffusion index values is calculated. a diagnoses is then made for the brain using one of predetermined categories of the neurological disorder by performing classification on a combination of the characteristic parameters via a classifier.

The present invention relates to an apparatus (10) to analyse diffusion magnetic resonance imaging data. The apparatus comprises an input unit (20), a processing unit (30), and an output unit (40). The input unit is configured to provide the processing unit with at least one diffusion magnetic resonance imaging “dMRI” image of a patient's brain. The processing unit is configured to determine an estimate of an orientation of neurons at each voxel in the dMRI image, the determination comprising utilization of the at least one dMRI image. The processing unit is configured to determine a plurality of fiber tracts in the at least one dMRI image, the determination comprising utilization of the estimated orientation of neurons at each voxel in the at least one dMRI image. The processing unit is configured to select a plurality of voxels along at least one fiber tract of the plurality of fiber tracts. The processing unit is configured to determine a neurological disease classification, the determination comprising utilization of at least one diffusivity feature associated with each of the selected plurality of voxels. The output unit is configured to output the neurological disease classification.

Aspects of the disclosure provide a method for denoising an image. The method can include receiving an acquired image from an image acquisition system, and processing the acquired image with a nonlinear diffusion coefficient based filter having a diffusion coefficient that is calculated using gradient vector orientation information in the acquired image.

A method of quantitatively evaluating a cognitive status and its future change from a medical image of an individual's brain, the method comprising scanning the individual's brain with a scanning device so as to acquire at least one medical brain image; processing the medical brain image to obtain at least one feature of the image; using a pre-established prediction model to determine a condition of the cognitive status and predict its future change based on the at least one feature obtained.

The present disclosure provides a method for identifying sites for administering cells in cell therapy for central nervous system damage in a subject, comprising the steps of: A) acquiring image data on at least part of the subject's brain, with an imaging device; B) obtaining information on the subject's brain, with a computer device in communication with the imaging device; C) using the image data and data pertaining to the subject's brain acquired by the computer device to depict motor fibers; D) identifying damaged locations where motor fibers have suffered damage, with the computer device, identifying a portion where motor fiber run-data is lower than another portion and identifying the lower portion as being motor fibers that have suffered damage; E) using the computer device to select, as sites of administration, safe regions near the damaged locations; and F) outputting, as graphic display, the selected sites of administration.

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.

The present disclosure provides methods and systems for image-based nerve fiber extraction. The methods may include obtaining an anatomical image of a subject and a diffusion image of the subject. The subject may include at least one region of interest (ROI) that relates to extraction of at least one target nerve fiber in the subject. The methods may further include determining, based on the anatomical image, the at least one ROI in the diffusion image; and extracting, from the diffusion image, at least one of the at least one target nerve fiber based on the at least one ROI.

The application provides a method, apparatus and computer program product for denoising a magnetic resonance diffusion tensor, wherein the method comprises: collecting data of K space; calculating a maximum likelihood estimator of a diffusion tensor according to the collected data of K space; calculating a maximum posterior probability estimator of the diffusion tensor by using sparsity of the diffusion tensor and sparsity of a diffusion parameter and taking the calculating maximum likelihood estimator as an initial value; and calculating the diffusion parameter according to the calculated maximum posterior probability estimator. The application solves the technical problem in the prior art of how to realize high precision denoising of diffusion tensor while not increasing scanning time and affecting spatial resolution, achieves the technical effects of effectively suppressing noises in the diffusion tensor and improving the estimation accuracy of the diffusion tensor.

A fat suppressed diffusion image determination apparatus, a corresponding method and a corresponding computer program determine a diffusion weighted magnetic resonance image (DWI) of an object. The fat suppressed diffusion image determination apparatus includes a diffusion reference image providing unit for providing a diffusion reference MR image of the object, a fat image determination unit for determining a fat image from the diffusion reference MR image, a diffusion weighted image providing unit for providing a diffusion weighted MR image of the object, a fat suppressed image determination unit for determining a fat suppressed diffusion weighted MR image using a combination of the diffusion weighted MR image and the fat image.