To provide a recording medium capable of estimating the early signs of diseases relating to amyloid β without using a PET image, an information processing device, an information processing method, a trained model generation method, and a correlation image output device.

The recording medium recording the computer program causes a computer to execute processes of: acquiring an MRI image of a subject; and inputting the acquired MRI image to a trained model that outputs a correlation image representing a correlation between a magnetic susceptibility capable of being specified on the basis of the MRI image and amyloid β in a case where the MRI image is input, and outputting the correlation image representing the correlation between the magnetic susceptibility of the subject and amyloid β.

A diagnosis support device includes a processor and a memory connected to or built in the processor. The processor is configured to perform non-linear registration processing of a target image which is a medical image to be analyzed and at least one representative image generated from a plurality of medical images, input at least one of transformation amount information of the target image obtained by the non-linear registration processing or a feature amount derived from the transformation amount information to a disease opinion derivation model, and output a disease opinion from the disease opinion derivation model.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a reservoir computing neural network. In one aspect there is provided a reservoir computing neural network comprising: (i) a brain emulation sub-network, and (ii) a prediction sub-network. The brain emulation sub-network is configured to process the network input in accordance with values of a plurality of brain emulation sub-network parameters to generate an alternative representation of the network input. The prediction sub-network is configured to process the alternative representation of the network input in accordance with values of a plurality of prediction sub-network parameters to generate the network output. The values of the brain emulation sub-network parameters are determined before the reservoir computing neural network is trained and are not adjusting during training of the reservoir computing neural network.

Systems and methods for detecting complex networks in MRI image data in accordance with embodiments of the invention are illustrated. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device.

A target location for a therapeutic intervention is determined in a subject with a neurological disorder. The target location is selected within at least one resting state network (RSN) map according to a predetermined criterion for the neurological disorder. The at least one RSN map includes a plurality of functional voxels within a brain of the subject, and each functional voxel of the plurality of functional voxels is associated with a probability of membership in an RSN. Instructions are transmitted to a treatment system that cause operation to be performed on the selected target location.

An automatic clipping technique capable of satisfactorily extracting blood vessels to be extracted is provided. A specific tissue extraction mask image which is created by extracting a specific tissue (for example, a brain) from a three-dimensional image acquired by magnetic resonance angiography and a blood vessel extraction mask image which is created by extracting a blood vessel from an area (a blood vessel search area) which is determined using a preset landmark position and the specific tissue extraction mask image are integrated to create an integrated mask. By applying the integrated mask to the three-dimensional image, a blood vessel is clipped from the three-dimensional image.

Methods and apparatus for evaluating an impact of injury to brain networks or regions are provided. The method comprises receiving MRI data of a brain of an individual, including a first volumetric dataset recorded using first imaging parameters and a second volumetric dataset recorded using second imaging parameters, combining, on a voxel-by-voxel basis, first MRI data based on the first volumetric dataset and second MRI data based on the second volumetric dataset to produce a volumetric injury map, performing a structural-functional analysis of one or more brain networks or regions by refining the volumetric injury map using a volumetric eloquence map that specifies eloquent brain tissue within the one or more brain networks or regions to determine an impact of injury within the one or more brain networks or regions, and displaying a visualization of the determined impact of injury within the one or more brain networks or regions.

Some methods of analyzing one or more brain lesions of a patient comprise, for each of the lesion(s), calculating one or more lesion characteristics from a first 3-dimensional (3D) representation of the lesion obtained from data taken at a first time and a second 3D representation of the lesion obtained from data taken at a second time that is after the first time. The characteristic(s) can include a change, form the first time to the second time, in the lesion's volume and/or surface area, the lesion's displacement from the first time to the second time, and/or the lesion's theoretical radius ratio at each of the first and second times. Some methods comprise characterizing whether the patient has multiple sclerosis and/or the progression of multiple sclerosis in the patient based at least in part on the calculation of the lesion characteristic(s) of each of the lesion(s).

Provided are a method, server, and computer program for designing a customized headgear for transcranial direct current stimulation. According to one embodiment, there is provided a method for designing a customized headgear for transcranial direct current stimulation performed by a computing device and applying electrical stimulation to a preset target point in a brain of a subject, the method including: acquiring a head image of the subject; generating a headgear mask by using the acquired head image; generating a stimulator mask by using an optimal stimulation position combination for applying the electrical stimulation to the preset target point; and generating a customized headgear mask for the subject by performing a subtraction operation between the generated headgear mask and the generated stimulator mask.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.