Systems and methods for estimating quantitative histological features of a subject's tissue based on medical images of the subject are provided. For instance, quantitative histological features of a tissue are estimated by comparing medical images of the subject to a trained model that relates histological features to multiple different medical image contrast types, whether from one medical imaging modality or multiple different medical imaging modalities. In general, the trained model is generated based on medical images of ex vivo samples, in vitro samples, in vivo samples or combinations thereof, and is based on histological features extracted from those samples. A machine learning algorithm, or other suitable learning algorithm, is used to generate the trained model. The trained model is not patient-specific and thus, once generated, can be applied to any number of different individual subjects.

There is provided a computer implemented method of automatic segmentation of three dimensional (3D) anatomical region of interest(s) (ROI) that includes predefined anatomical structure(s) of a target individual, comprising: receiving 3D images of a target individual, each including the predefined anatomical structure(s), each 3D image is based on a different respective imaging modality. In one implementation, each respective 3D image is inputted into a respective processing component of a multi-modal neural network, wherein each processing component independently computes a respective intermediate, and the intermediate outputs are inputted into a common last convolutional layer(s) for computing the indication of segmented 3D ROI(s). In another implementation, each respective 3D image is inputted into a respective encoding-contracting component a multi-modal neural network, wherein each encoding-contracting component independently computes a respective intermediate output. The intermediate outputs are inputted into a single common decoding-expanding component for computing the indication of segmented 3D ROI(s).

A device and system for supporting a patient or an object in an examination is provided. The supporting system may include a portion for supporting the body of a patient and/or a head supporting device. The portion for supporting the body may move in one or more directions. The head supporting device may be adjust to meet requirements of imaging when the patient or the object is supine or prone.

A computer-implemented tumor position determining model is trained, based on a plurality of sets of image data, to determine a subsequent position of a tumor in a subject based on a subsequent 2D or 3D representation of a surface of the subject, an initial image of the tumor in the subject and an initial 2D or 3D representation of a surface of the subject. Each set of image data comprises an initial training image of a tumor in a subject, an initial training 2D or 3D representation of a surface of the subject, a subsequent training image of the tumor in the subject and a subsequent training 2D or 3D representation of a surface of the subject. The subsequent training image and the subsequent training 2D or 3D representation are taken at a subsequent point in time than the initial training image and the initial training 2D or 3D representation and the plurality of sets of image data are from a plurality of different subjects.

Systems and methods for high-bandwidth, minimally invasive brain-computer interfaces (BCIs) are disclosed. The BCIs are configured for deployment and operation in conjunction with a comprehensive interventional electrophysiology procedural suite. Three primary methods of minimally invasive electrode array delivery are disclosed: (1) cortical surface delivery, (2) ventricular delivery, and (3) endovascular delivery. Additionally, systems and methods for interacting with such high-bandwidth electrode arrays are discussed, including real-time imaging, signal processing, and neural decoding. Systems and methods for architectures for accelerating the underlying computational processes (such as graphics processing units or tensor processing units) are also discussed. Multiple applications of BCIs are discussed, with emphasis on restoration, rehabilitation, and augmentation of neurologic function.

A cerebral infarction treatment support system (100) includes a detection device (10), a display (30), and an image controller (20), and the image controller (20) includes a receiver (21) configured to receive at least one of first information (41) generated by the detection device (10) or second information (42) related to a susceptibility gene generated based on the first information (41), and a video output (22) configured to output at least one of the received first information (41) or second information (42) to the display (30).

A method is for actuating a medical imaging device for generating a second three-dimensional image dataset including a target region in a region of interest of a patient with a functional impairment. The method includes providing a first three-dimensional image dataset including the region of interest of the patient; identifying the target region based on the first three-dimensional image dataset, a partial region of the region of interest with the functional impairment being determined; determining an imaging parameter for generating the second three-dimensional image dataset based on the identified target region; and actuating the medical imaging device based on the imaging parameter for the generation of the second three-dimensional image dataset.

At least one example embodiment relates to a computer-implemented method for providing stroke information, the method comprising receiving computed tomography imaging data of an examination area of a patient, the examination area of the patient comprising a plurality of brain regions, at least one brain region of the plurality of brain regions being affected by a stroke, receiving brain atlas data, generating registered imaging data based on the computed tomography imaging data and the brain atlas data, the registered imaging data being registered to the brain atlas data, generating the stroke information regarding the stroke based on a set of algorithms and the registered imaging data, and providing the stroke information.

A diagnostic method for determining if a patient is viable candidate for a surgical procedure to permanently eliminate migraine headache pain. The method includes determining if a patient's pain is a migraine pain condition or pain from another medical condition and determining an anatomical location of the determined migraine pain condition. Once the anatomical location is determined, the method correlates the determined anatomical location of the migraine pain condition to a root cause nasal/sinus location and determines a root cause nasal/sinus condition that is causing the patient's migraine pain. Once these diagnostic procedures are complete, the patient may be scheduled for surgery to eliminate the root cause nasal/sinus condition, and thus, permanently eliminate the migraine headache pain.

The present disclosure provides an operation method of a PET (positron emission tomography) quantitative localization system, which includes steps as follows. The PET image and the MRI (magnetic resonance imaging) of the patient are acquired; the nonlinear deformation is performed on the MRI and the T1 template to generate deformation information parameters; the AAL (automated anatomical labeling) atlas is deformed to an individual brain space of the patient, so as to generate an individual brain space AAL atlas, where the AAL atlas and the T1 template are in a same space; lateralization indexes of the ROIs of the individual brain space AAL atlas corresponding to the PET image normalized through the gray-scale intensity are calculated; the lateralization indexes are inputted into one or more machine learning models to analyze the result of determining a target.