Disclosed in embodiments of the present application are a method and a product for AI processing of tumor based on VRDS 4D medical images, which is applied to medical imaging apparatuses, and the method includes: determining a bitmap BMP data source according to a plurality of scanned images associated with a target organ of a target user, generating target medical image data according to the BMP data source; determining abnormal data in target medical image data; determining the attribute information of a tumor of the target user according to the abnormal data; performing 4D medical imaging according to the target medical image data, and outputting the attribute information of the tumor. The embodiment of this application is facilitated to improve the accuracy and efficiency of tumor recognition.

The majority of image reconstruction algorithms are common to DT and CT, and require reconstruction volume allocation and are based on ray tracing techniques. Reconstructed three-dimensional images become available only after the entire volume is processed and the algorithm completes. The present invention performs reconstruction on a slice-by-slice basis, instead of waiting for completion of the algorithm by back-projecting each pixel in each attenuation image towards the emitter that generated that image, onto a selected reconstruction slice and determining a proportion of overlap with grid cells in the slice to obtain weighting factors in order to calculate an average back-projected intensity for each grid cell in the selected reconstruction slice.

Disclosed in the present application are a method and a product for AI processing of tumor and blood vessel based on VRDS 4D medical images, including determining a bitmap BMP data source; generating target medical image data according to the BMP data source; determining abnormal data in the target medical image data, a target tissue includes the target organ and a blood vessel; determining an association relationship between a tumor and a blood vessel of the target user according to a data set of the blood vessel and the abnormal data; performing 4D medical imaging according to the target medical image data, and outputting the association relationship between the tumor and the blood vessel, which is facilitated to improve the accuracy and efficiency of the medical imaging apparatus in recognizing the association relationship between a tumor and a blood vessel.

A method and a product for AI processing of artery and vein based on VRDS 4D medical images, the method is applied to a medical imaging apparatus, and the method includes: the medical imaging apparatus first determines a bitmap (BMP) data source according to a plurality of scanned images of a target site of a target user, second generates target medical image data according to the BMP data source, and finally performs 4D medical imaging according to the target medical image data, wherein the target medical image data includes at least a data set of a blood vessel in the target site, and a data set of a vein is a transfer function result of a cubic space of a surface of the vein and a tissue structure inside the vein, and an intersection position of the artery and a vein presents an overall separation image effect.

An MRI image processing and analysis system may identify instances of structure in MRI flow data, e.g., coherency, derive contours and/or clinical markers based on the identified structures. The system may be remotely located from one or more MRI acquisition systems, and perform: error detection and/or correction on MRI data sets (e.g., phase error correction, phase aliasing, signal unwrapping, and/or on other artifacts); segmentation; visualization of flow (e.g., velocity, arterial versus venous flow, shunts) superimposed on anatomical structure, quantification; verification; and/or generation of patient specific 4-D flow protocols. A protected health information (PHI) service is provided which de-identifies medical study data and allows medical providers to control PHI data, and uploads the de-identified data to an analytics service provider (ASP) system. A web application is provided which merges the PHI data with the de-identified data while keeping control of the PHI data with the medical provider.

Described here are systems and methods for generating quantitative flow mapping from medical flow data (e.g., medical images, patient-specific computational flow models, particle image velocimetry data, in vitro flow phantom) over a virtual volume representative of a catheter or other medical device. As such, quantitative flow mapping is provided with reduced computational burdens. Quantitative flow maps can also be generated and displayed in a manner that is similar to catheter-based or other medical device-based mapping, without requiring an interventional procedure to place the catheter or medical device.

Systems and methods for providing improved eddy current correction (ECC) in medical imaging environments. One or more of the embodiments disclosed herein provide a deep learning-based convolutional neural network (CNN) model trained to automatically generate an ECC mask which may be composited with two-dimensional (2D) scan slices or four-dimensional (4D) scan slices and made viewable through, for example, a web application, and made manipulable through a user interface thereof.

From a plurality of medical images in time phases, a target site is extracted from at least one medical image, a reference point is set on each of a target-site side, and a periphery side of the target site which are on across from each other over an outline of the extracted target site, and movement information for the reference points is calculated.

Devices, systems, computer program products and computer implemented methods are provided for dynamically assessing a moving object from a sequence of consecutive volumetric image frames of such object, which images are timely separated by a certain time interval, by: identifying in at least one image of the sequence the object of interest; segmenting the object to identify object contour; propagating the object contour as identified to other images of the sequence; and performing dynamic analysis of the object based on the object contour as propagated.

A method and a product for AI endoscope analyzing of vein based on VRDS 4D medical images, which is applied to medical imaging apparatus, and the method includes: 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 a target vein to be observed and an artery, a kidney and a hepatic portal associated with the target vein; generating first medical image data according to the BMP data source; generating second medical image data according to the first medical image data; processing the second medical image data to obtain target medical image data; extracting a data set of the target vein in the target medical image data; performing 4D medical imaging according to the data set of the target vein to display an internal image of the target vein.