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 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 system and method may include determining a target stimulation volume based on modifying a patient population image for which an efficacious volume had been determined. A system and method for suggesting stimulation devices may include determining which stimulation device is capable of producing an output volume of activation that most closely matches the target volume. A system and method for facilitating selection of stimulation parameters may include graphically identifying a maximum volume in which tissue is stimulatable by an implanted stimulation device. A system and method may pre-compute volumes of activation that result from a predetermined modification of programming settings. A system and method may transmit stimulation programming settings from a stimulation programming module to a stimulation generating device.

Exemplary system, method and computer-accessible medium for determining a difference(s) between two sets of subjects, can be provided. Using such exemplary system, method and computer-accessible medium, it is possible to receive first imaging information related to a first set of subjects of the two sets of the subjects, receive second imaging information related to a second set of subjects of the two sets of subjects, generate third information by performing a decomposition procedure(s) on the first imaging information and the second information, and determine the difference(s) based on the third information.

A method of predicting a brain atrophy condition for an individual using the individual's predicted age difference (PAD). The method comprises acquiring at least one brain image of the individual; processing the brain image to obtain at least one feature of the brain image; generating a PAD value of the individual based on the at least one feature of the image; and determining a brain atrophy condition of the individual based on the PAD 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 g.sub.l(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?(y, z) is given by |M.sub.nl(t)|=|?.sub.0.sup.tg.sub.l(t)t.sup.ndt|, 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?(y, z): |M.sub.nl(t)|?T.sub.n for each 0?n?m, wherein m is an integer order equal to or greater than 1.

There is provided a technique for DWI measurement, in which MPG application is performed in many directions, that enables detection of presence or absence of body motion during imaging without prolongation of imaging time. A plurality of image groups each comprising 6 or more diffusion-weighted images selected from a plurality of diffusion-weighted images are created so the groups differ from one anther in one or more diffusion-weighted images included in each of the groups. Value of a predetermined diffusion index representing a characteristic amount of diffusion-weighted image is calculated for each image group from the diffusion-weighted images included in each image group. Value of a predetermined body motion index relating to body motion information is calculated from the value of the diffusion index for each image group. Presence or absence of body motion is determined for each image group on the basis of the value of the body motion index.

The present disclosure provides a method and system for processing multi-modality images. The method may include obtaining multi-modality images; registering the multi-modality images; fusing the multi-modality images; generating a reconstructed image based on a fusion result of the multi-modality images; and determining a removal range with respect to a focus based on the reconstructed image. The multi-modality images may include at least three modalities. The multi-modality images may include a focus.

Devices, systems, methods for validating ablation results in a patient's brain are provided. In some embodiments, the method for validating ablation result in a patient's brain includes obtaining magnetic resonance (MR) data of the patient's brain, by use of a magnetic resonance imaging (MRI) device; obtaining first imaging data of the patient's brain, by use of the MRI device; extracting, by use of computing device in communication with the MM device, first fiber tracts passing through an anatomy in the patient's brain based on the first imaging data; obtaining, by use of the MRI device, second imaging data of the patient's brain after ablation of the anatomy in the patient's brain has started; extracting second fiber tracts passing through the anatomy in the patient's brain based on the second imaging data; and outputting a graphical representation of a comparison between the first fiber tracts and the second fiber tracts.

Methods and systems for diagnosing cancer in the prostate and other organs are disclosed. Exemplary methods comprises extracting texture information from MRI imaging data for a target organ, sometimes using two or more different imaging modalities. Texture features are determined that are indicative of cancer by identifying frequent texture patterns. A classification model is generated based on the determined texture features that are indicative of cancer, and diagnostic cancer prediction information for the target organ is then generated to help diagnose cancer in the organ.