A method including determining a cerebral ischemia region of a patient based on a magnetic resonance diffusion weighted imaging of the patient; determining a DWI gray scale distribution parameter in a region with low ADC values in the magnetic resonance diffusion weighted imaging; and judging whether the DWI in the region with low ADC values in the magnetic resonance diffusion weighted imaging and the ADC values in the region with low ADC values in the magnetic resonance diffusion weighted imaging are mismatched according to the DWI gray scale distribution parameter in the region with low ADC values in the magnetic resonance diffusion weighted imaging is disclosed. The method provides a more scientific and objective basis for making a decision on whether the acute cerebral ischemia patient should be treated with thrombolysis, thereby improving a cure rate of the cerebral ischemia patient.

Disclosed are a hearing state prediction apparatus and method based on a diffusion tensor image, including: obtaining a diffusion tensor image to be processed, wherein the diffusion tensor image comprises a diffusion-weighted image; generating a diffusion index image based on the diffusion-weighted image; determining a white matter microstructural feature corresponding to the diffusion tensor image, and determining a hearing state corresponding to the diffusion tensor image according to a mapping relationship between the white matter microstructural feature and the hearing state. The apparatus may generate the diffusion index image through the diffusion-weighted image included in the diffusion tensor image, and then the white matter microstructural feature is determined based on the diffusion index image, so that the white matter microstructural feature can be identified more accurately, and the accuracy of a hearing state evaluation result is improved. Meanwhile, the relation between hearing disorder and brain microstructure change is disclosed.

Systems and methods for registration degradation correction for surgical procedures are disclosed. An example system includes a processor and a surgical camera configured to record images of a patient. The processor is configured to perform an initial patient registration that registers a patient volume space of virtual positional data points to physical positional points of at least a portion of the patient. The processor also identifies or receives an indication of an identification of a natural patient mark using recorded images of the patient and records a virtual mark in the patient volume space in response to a received activation action based on the identification of the natural patient mark. The processor then causes the patient volume space of virtual positional data points and the recorded virtual mark to be displayed in an overlaid manner over the recorded images on a single display.

A method of predicting an effect of a chemotherapy treatment on a cancer patient's cognitive status using the patient's predicted age difference (PAD) comprises acquiring at least one medical brain image of a patient's brain before a chemotherapy treatment; processing the medical brain image to obtain at least one feature of the image; generating a PAD value of the individual based on the at least one feature of the image; and predicting an effect of the chemotherapy treatment on a cancer patient's cognitive status using the PAD value.

A system and method for diffusion weighted magnetic resonance measurement includes performing a diffusion encoding sequence that comprises a diffusion encoding time-dependent magnetic field gradient g(t) with non-zero components g.sub.l(t) along two orthogonal directions (y, z), and a b-tensor having at least two non-zero eigenvalues. The gradient g(t) comprises a first and second encoding block. 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 y, |M.sub.ny(t)|T.sub.n for each 0nm, where T.sub.n is a predetermined n-th order threshold, and, along z, |M.sub.nz(t)|T.sub.n for each 0 n m1 and |M.sub.nz(t)|>T.sub.n for n=m. The second encoding block is adapted to yield, at an end of the second encoding block, along each one of l(y,z): |M.sub.nl(t)|T.sub.n for each 0n m, wherein m is an integer order equal to or greater than 1.

A Multimodality Brain Mapping System (MBMS), comprising one or more scopes (e.g., microscopes or endoscopes) coupled to one or more processors, wherein the one or more processors obtain training data from one or more first images and/or first data, wherein one or more abnormal regions and one or more normal regions are identified; receive a second image captured by one or more of the scopes at a later time than the one or more first images and/or first data and/or captured using a different imaging technique; and generate, using machine learning trained using the training data, one or more viewable indicators identifying one or abnormalities in the second image, wherein the one or more viewable indicators are generated in real time as the second image is formed. One or more of the scopes display the one or more viewable indicators on the second image.

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.

A method of analyzing a multidimensional image tensor containing a plurality of images comprises: performing imaging scans of a subject to obtain imaging data; generating the multidimensional image tensor from the imaging data; determining a spatial basis tensor containing basis images based on the multidimensional image tensor; determining a temporal basis tensor containing basis functions for a temporal dimension based on the multidimensional image tensor; determining a core tensor that relates the spatial basis tensor to the temporal basis tensor; pre-multiplying the core tensor and the temporal basis tensor to produce a modified temporal basis tensor; storing the spatial basis tensor and the modified temporal basis tensor; and generating an image by multiplying at least (i) at least a portion of the spatial basis tensor and (ii) at least a portion of the modified temporal basis tensor.

An image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires a magnetic resonance (MR) image in which objects of interest scattered in a brain of a subject are rendered. The processing circuitry acquires connection information indicating connectivity among a plurality of regions of the brain. The processing circuitry performs an analysis with use of the MR image and the connection information, and calculates analytical values related to the objects of interest and allocated to the regions.

A method and apparatus for unsupervised cross-modal medical image synthesis is disclosed, which synthesizes a target modality medical image based on a source modality medical image without the need for paired source and target modality training data. A source modality medical image is received. Multiple candidate target modality intensity values are generated for each of a plurality of voxels of a target modality medical image based on corresponding voxels in the source modality medical image. A synthesized target modality medical image is generated by selecting, jointly for all of the plurality of voxels in the target modality medical image, intensity values from the multiple candidate target modality intensity values generated for each of the plurality of voxels. The synthesized target modality medical image can be refined using coupled sparse representation.