A system and method for navigating to a target using fluoroscopic-based three dimensional volumetric data generated from two dimensional fluoroscopic images, including a catheter guide assembly including a sensor, an electromagnetic field generator, a fluoroscopic imaging device to acquire a fluoroscopic video of a target area about a plurality of angles relative to the target area, and a computing device. The computing device is configured to receive previously acquired CT data, determine the location of the sensor based on the electromagnetic field generated by the electromagnetic field generator, generate a three dimensional rendering of the target area based on the acquired fluoroscopic video, receive a selection of the catheter guide assembly in the generated three dimensional rendering, and register the generated three dimensional rendering of the target area with the previously acquired CT data to correct the position of the catheter guide assembly.

A method and apparatus is provided that uses a deep learning (DL) network together with a multi-resolution detector to perform X-ray projection imaging to provide improved resolution similar to a single-resolution detector but at lower cost and less demand on the communication bandwidth between the rotating and stationary parts of an X-ray gantry. The DL network is trained using a training dataset that includes input data and target data. The input data includes projection data acquired using a multi-resolution detector, and the target data includes projection data acquired using a single-resolution, high-resolution detector. Thus, the DL network is trained to improve the resolution of projection data acquired using a multi-resolution detector. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., noise and artifacts).

A method, system, and computer readable medium to compensate for consecutive missing views in Computed Tomography (CT) reconstruction. By utilizing at least one complementary ray from a previous or subsequent view, the missing view(s) can be filled in. When plural complementary rays exist, a linear or non-linear combination of rays can be used to fill in the missing views, and the weights used in the combination may be smoothed to prevent over-emphasis of the replacement views.

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

An apparatus is configured to receive input image data corresponding to output image data of a first radiology scanner device, translate the input image data into a format corresponding to output image data of a second radiology scanner device and generate an output image corresponding to the translated input image data on a post processing imaging device associated with the first radiology scanner device. Medical images from a new scanner can be translate to look as if they came from a scanner of another vendor.

Disclosed is an X-ray image processing apparatus including a data obtaining unit generating first to N-th images indicating an internal structure of an object and an image processing unit receiving the first to N-th images from the data obtaining unit, detecting a movement of the object, and generating a final image from the first to N-th images based on the movement of the object. The data obtaining unit actively controls an X-ray pulse irradiated based on the movement of the object.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

An object volume acquisition method of an ultrasonic image, for a probe of an ultrasonic system is disclosed. The volume acquisition method of the object in the ultrasonic image includes collecting, by the probe, a plurality of two-dimensional ultrasonic images; obtaining the plurality of two-dimensional ultrasonic images, an offset angle, a rotation axis and a frequency of the probe corresponding to the plurality of two-dimensional ultrasonic images; segmenting a first image including an ultrasonic image object from each two-dimensional ultrasonic image of the plurality of two-dimensional ultrasonic images based on a deep learning structure; determining a contour of the ultrasonic image object; reconstructing a three-dimensional model corresponding to the ultrasonic image object according to the contour of the ultrasonic image object corresponding to the each two-dimensional ultrasonic image; and calculating a volume of the ultrasonic image object according to the three-dimensional model corresponding to the ultrasonic image object.

A marker-coordinate detecting unit detects coordinates of a stent marker on a new image when the new image is stored in an image-data storage unit; and then a correction-image creating unit creates a correction image from the new image through, for example, image transformation processing, so as to match up the detected coordinates with reference coordinates that are coordinates of the stent marker already detected by the marker-coordinate detecting unit in a first frame. An image post-processing unit then creates an image for display by performing post-processing on the correction image created by the correction-image creating unit, the post-processing including high-frequency noise reduction filtering-processing, low-frequency component removal filtering-processing, and logarithmic-image creating processing; and then a system control unit performs control of displaying a moving image of an enlarged image of a set region that is set in the image for display, together with an original image.

A radiological imaging device that includes a source that emits radiation that passes through at least part of a patient, the radiation defining, a central axis of propagation; and a receiving device that receives the radiation and is arranged on the opposite side of the patient with respect to the source. The receiving device includes a first detector to detect radiation when performing at least one of tomography and fluoroscopy, a second detector to detect radiation when performing at least one of radiography and tomography; and a movement apparatus arranged to displace the first and second detectors with respect to the source. The movement apparatus provides a first active configuration in which the radiation hits the first detector and a second active configuration in which the radiation hits the second detector.