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.

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.

Diffusion-weighted data are acquired with an MRI system. From the diffusion-weighted data, a comprehensive diffusion tensor distribution (CDTD) is generated. The provides a proportional weighting, at the voxel level, of different diffusion tensors that could describe the water diffusion occurring in the voxel. The CDTD provides insight into tissue microstructure without making assumptions about the structure of a diffusion tensor used to characterize diffusion occurring in tissues of interest. Water pool images, corresponding to different subsets of diffusion tensors in the CDTD, may be generated to assess different components of water diffusion in tissue. Classification images can also be generated from the CDTD to depict different clusters of voxels having similar distributions of diffusion tensors.

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.

According to an aspect, there is provided a computer-implemented segmentation method (100, 210), the method comprising: performing a first automated segmentation operation (400) on one or more first images of a subject area to automatically determine a first segmentation map of the subject area, wherein the one or more first images are generated using a first technique; performing, at least partially based on the first segmentation map, a second automated segmentation operation (600) on one or more second images of the subject area to automatically determine a second segmentation map of the subject area, wherein the one or more second images of the subject area are generated using a second technique different from the first technique, the first and second imaging techniques to capture different properties of the subject area; automatically determining a mismatch between segmented portions of the first and second segmentation maps.

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 myelin image is generated with a stable contrast regardless of an imaging condition with an MRI apparatus that measures an echo signal generated from a subject by applying a high-frequency magnetic field and a gradient magnetic field to the subject placed in a static magnetic field according to a predetermined imaging sequence. A reconstructed image is obtained from the echo signal. A distribution of a quantitative value of the subject is estimated using a plurality of the reconstructed images, each of which is obtained by a plurality of types of imaging having different imaging conditions of the imaging sequence, and a signal function defining a relationship between the quantitative value of the subject and a signal value of the reconstructed image. An image generation unit generates an arbitrary image from the distribution of the quantitative value.

According to a first aspect of the present inventive concept, there is provided a method for performing a diffusion weighted magnetic resonance measurement on a sample, the method comprising: operating a magnetic resonance scanner to apply a diffusion encoding sequence to the sample; and operating the magnetic resonance scanner to acquire from the sample one or more echo signals; wherein the diffusion encoding sequence comprises a diffusion encoding time-dependent magnetic field gradient g(t) with non-zero components gl(t) along at least two orthogonal directions y and z, and a b-tensor having at least two non-zero eigenvalues, the magnetic field gradient comprising a first and subsequent second encoding block, wherein an n-th order gradient moment magnitude along direction l ϵ (y, z) is given by M.sub.nl(t)|=|∫.sub.0.sup.tgl(t′)t.sup.′n dt′| and the first encoding block is adapted to yield, at an end of the first encoding block: along said direction y, |M.sub.ny(t)|≤T.sub.n for each 0≤n≤m, where T.sub.n is a predetermined n-th order threshold, and along said direction z, |M.sub.nz(t)|<T.sub.n for each 0≤n≤m−1 and |M.sub.nz(t)|>T.sub.n for n=m; and the second encoding block is adapted to yield, at an end of the second encoding block: along each one of said directions l ϵ (y,z): |M.sub.nt(t)|≤Tn for each 0≤n≤m, wherein m is an integer order equal to or greater than 1.

A medical image processing apparatus including processing circuitry configured to: obtain from medical imaging measurements, observations of one or more vector or tensor valued fields as projected from one or more 2D acquisition planes; use an optimisation procedure to determine from the observations a superset of 3D fields (which may be scalar, vector, or tensor) via a solution ansatz constrained by a system of partial differential equations, and output the plurality of these fields.

A system and methods for labeling tractography, having a tract segmentation and a tract labeling atlas, in a presence of a lesion in a brain, involving: a graphical user interface (GUI) comprising a tool configured to facilitate adjusting a displacement for intraoperatively reperforming the tract segmentation in approximately real time, modeling deformation of the tract labeling atlas by facilitating modeling a force exerted by the lesion on the brain, and defining parameters of a size, a shape, and a level of the displacement condition; and a processor in communication with the GUI and configured to: determine whether an infiltration condition and a displacement condition appears in the tractography; if the infiltration condition and the displacement condition is determined to appear in the tractography, estimate the infiltration condition and the displacement condition; if the displacement condition is determined to appear in the tractography, instruct the GUI to render the tool and model the force exerted by the lesion on the brain by using the parameters, whereby a new tract segmentation and a new tract labeling atlas are provided; and if the displacement condition is determined to be absent from the tractography, refrain from modeling the force, a presence of only the infiltration condition being assumed.