A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.

An apparatus for stent visualization includes a hardware processor that is configured to input one or more stent images from a sequence of X-ray images and corresponding balloon marker location data to a cascaded spatial transform network. The background is separated from the one or more stent images using the cascaded spatial transform network and a transformed stent image with a clear background and a non-stent background image is generated. The stent layer and non-stent layer are generated using a neural network without online optimization. A mapping function f maps the inputs, the sequence images and marker coordinates, into the two single image outputs.

Methods and systems are provided for controlling an x-ray imaging system. In one embodiment, a method for an x-ray imaging system includes acquiring, with the x-ray imaging system, a first image as an x-ray tube current of the x-ray imaging system is ramping to a target x-ray tube current, determining a corrected brightness of the first image, the corrected brightness including a measured brightness of the first image corrected by a feedback x-ray tube current relative to the target x-ray tube current, and updating the target x-ray tube current based on the corrected brightness of the first image.

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.

Systems and methods are provided for processing X-ray images, wherein the methods are implemented as a software program product executable on a processing unit of the systems. Generally, an X-ray image is received by the system, the X-ray image being a projection image of a first object and a second object. The first and second objects are classified and a respective 3D model of the objects is received. At the first object, a geometrical aspect like an axis or a line is determined, and at the second object, another geometrical aspect like a point is determined. Finally, a spatial relation between the first object and the second object is determined based on a 3D model of the first object, a 3D model of the second object, and the information that the point of the second object is located on the geometrical aspect of the first object.

A high contrast instrument, such as a radiopaque portion, can be captured and/or viewed in an image that is acquired with an imaging system, such as with a fluoroscopic imaging system. A statistical model can be used to assist in identifying a possible or probable location of a target. A user may move the instrument coil to the statistically probable location of the target to, for example, perform a procedure or carry out a task.

Discussed herein are devices, systems, and methods for organ mask generation. A device, system and method for organ mask generation including generating a synthetic centroid mask, identifying first and second intensity thresholds, in a first segmentation pass, setting (i) pixels of an image with intensities less than the first threshold to zero and (ii) pixels of the image corresponding to objects with centroids outside the synthetic centroid mask to zero, resulting an initial organ mask, in a second segmentation pass, setting pixels (i) with intensities less than the second threshold, the second threshold less than the first threshold to zero and (ii) setting pixels corresponding to objects with centroids outside the initial organ mask to zero, resulting in a second organ mask, and expanding and filling the second organ mask to generate an organ mask.

A time sequenced series of optical images of a patient is obtained at a rate faster than cardiac frequency, wherein the time sequenced series of images capture one or more physical properties of intrinsic contrast. A cross-correland signal from the patient is obtained. A cross-correlated wavelet transform analysis is applied to the time sequenced series of optical images to yield a spatiotemporal representation of cardiac frequency phenomena. The cross-correlated wavelet transform analysis comprises performing a wavelet transform on the time-sequenced series of optical images to obtain a wavelet transformed signal, cross-correlating the wavelet transformed signal with the cross-correland signal to obtain a cross-correlated signal, filtering the cross-correlated signal at cardiac frequency to obtain a filtered signal, and performing an inverse wavelet transform on the filtered signal to obtain a spatiotemporal representation of the time sequenced series of optical images. Images of the cardiac frequency phenomena are generated.

Disclosed herein are systems and methods for adjusting appearance of objects in medical images.

A console of a mobile radiography apparatus includes a CPU that acquires a fluoroscopic image captured by a radiation detector and a visible light image captured by a visible light camera as a moving image related to the capture of the fluoroscopic image of a subject by the mobile radiography apparatus. The CPU extracts a frame to be subjected to a support process, which is a diagnosis support process or an imaging support process, from the moving image. A GPU executes the support process for the extracted frame.