A method and a product for AI recognizing of embolism based on VRDS 4D medical image, the method is applied to a medical imaging apparatus, and the method includes the following steps: determining a bitmap (BMP) data source according to a plurality of scanned images of a target site of a target user, wherein the target site includes an embolism formed on a wall of a target blood vessel; generating target medical image data according to the BMP data source; performing 4D medical imaging according to the target medical image data and determining a feature attribute of the embolism according to an imaging result, wherein the feature attribute includes at least one of the following: density, crawling direction, correspondence with a site of cancer focus and edge characteristics; and determining a type of the embolism according to the features and outputting the type.

A non-transitory computer-readable medium stores a program capable of improving the accuracy of detection of a target object. The program causes a computer to execute operations including, acquiring data in which a physical quantity is associated with each unit area acquired by dividing a given space; setting a detection region in a time space of three or more dimensions in the space or the time space; setting a control region at a position surrounding a gap with the gap surrounding the detection region disposed in a space having the same dimensions as those of the detection region; and determined whether or not one or more unit areas included in the detection region are predetermined areas on the basis of comparison between physical quantities of one or more unit areas included in the detection region and the control region that are set.

An X-ray imaging system using multiple pulsed X-ray sources to perform highly efficient and ultrafast 3D radiography is presented. There are multiple pulsed X-ray sources mounted on a structure in motion to form an array of sources. The multiple X-ray sources move simultaneously relative to an object on a pre-defined arc track at a constant speed as a group. Electron beam inside each individual X-ray tube is deflected by magnetic or electrical field to move focal spot a small distance. When focal spot of an X-ray tube beam has a speed that is equal to group speed but with opposite moving direction, the X-ray source and X-ray flat panel detector are activated through an external exposure control unit so that source tube stay momentarily standstill equivalently. 3D scan can cover much wider sweep angle in much shorter time and image analysis can also be done in real-time.

A method of imaging nervous tissue, comprising acquiring functional imaging modality data from a functional imaging modality which images an intrabody volume of a patient having a body part, the patient having been injected with an imaging agent having a nervous tissue uptake by an autonomic nervous system (ANS); and locating the nervous tissue in the intrabody volume based on the functional imaging modality data.

The present invention relates to the field of medical imaging in the absence of contrast agents. In one form, the invention relates to the field of imaging vessels, particularly blood vessels such as the pulmonary vasculature and is suitable for use as a technique for detecting pulmonary embolism (PE), such as acute PE. Embodiments of the present invention provide improved image processing techniques having the capability to extract and use image data to overcome the need for contrast agents to distinguish between different types of tissue. Furthermore, it has also been realised that the image data accessed by the improved image processing can be used to identify irregularities in vessels.

The invention relates to a magnetic resonance imaging data processing system (126) for processing motion artifacts in magnetic resonance imaging data sets using a deep learning network (146, 502, 702) trained for the processing of motion artifacts in magnetic resonance imaging data sets. The magnetic resonance imaging data processing system (126) comprises a memory (134, 136) storing machine executable instructions (161, 164) and the trained deep learning network (146, 502, 702). Furthermore, the magnetic resonance imaging data processing system (126) comprises a processor (130) for controlling the magnetic resonance imaging data processing system. Execution of the machine executable instructions (161, 164) causes the processor (130) to control the magnetic resonance imaging data processing system (126) to: receive a magnetic resonance imaging data set (144, 500, 800), apply the received magnetic resonance imaging data set (144, 500, 800) as an input to the trained deep learning network (146, 502, 702), process one or more motion artifacts present in the received magnetic resonance imaging data set (144, 500, 800) using the trained deep learning network (146, 502, 702).

Systems, methods, and apparatus are disclosed for cardiac structure tracking. An example method includes segmenting a diaphragm or respiratory surrogate, heart, and target. The method also includes performing a peak-exhale to peak-inhale registration and generating a respiratory motion model. The method further includes tracking the diaphragm using X-ray imaging and estimating a target position for an x-ray guided cardiac radioablation treatment. The example method provides directly, precisely controlled x-ray guided cardiac radioablation that accurately targets the substrates of cardiac ablation while minimizing doses to healthy tissue.

The invention relates to a method of visualising a dynamic anatomical structure (1), a computer program and a user interface. The method comprises (a) providing a sequence of three-dimensional medical images (M1, M2, M3, . . . MZ) of a dynamic anatomical structure (1) spanning a time period (T), (b) providing a dynamic model (14), in particular surface of the anatomical structure, (c) determining a volume of interest (40) containing an anatomical feature of interest (3) within each of the three-dimensional images, wherein the volume of interest (40) follows the position and/or the shape of the anatomical feature of interest (3) across the time period and wherein the volume of interest (40) is smaller than the complete field of view of the three-dimensional medical images (M1, M2, M3, . . . MZ), and (d) providing a three-dimensional visualisation environment (50, 70), wherein a visualisation (45) corresponding to a particular point in time comprises (i) a volume rendering of the volume of interest (40) of the three-dimensional image; and (ii) a visualisation of the dynamic model (14) in the same coordinate system. Preferably, the three-dimensional visualisation environment (50, 70) allows for displaying the dynamic model (14) and the volume rendered volume of interest (40) for each three-dimensional image across the time period in cine mode.

A system and method for dynamic validation, registration correction for surgical navigation during medical procedures involving confirmation of registration between previously registered virtual objects, in a common coordinate frame of a surgical navigation system and an operating room, and intra-operatively acquired imaging during the medical procedure in the common coordinate frame. The method involves displaying intra-operatively acquired imaging of the surgical field, containing the real objects corresponding to the previously registered virtual objects, with the real objects being tracked by a tracking system. The method involves overlaying a virtual image containing the previously registered virtual objects onto the intra-operatively acquired imaging, from the point of view of the intra-operatively acquired imaging, and detecting any misalignment between any the previously registered virtual objects contained in the virtual image and its corresponding real object contained in the intra-operatively acquired imaging.

Provided is a system and method for estimating the motion of a target inside a tissue based on surface deformation of the soft tissue. The system consists of an acquisition unit, a reference input unit, two surface extraction units, a target position extraction unit, a feature calculation unit, and a target motion estimation unit. The method includes: the acquisition unit acquires an image I.sub.i of the soft tissue; the surface extraction unit extracts a surface f.sub.i of the soft tissue from I.sub.i; the reference input unit acquires a reference image I.sub.ref of the soft tissue; the surface extraction unit and the target position extraction unit respectively extract a reference surface f.sub.ref of the soft tissue and a target reference position t.sub.ref from I.sub.ref, the feature calculation unit calculates deformation feature Ψ.sub.i of f.sub.i relative to f.sub.ref, the target motion estimation unit estimates the target displacement based on Ψ.sub.i and t.sub.ref.