Systems and methods provide guidance for selection of projection perspectives to utilize to obtain complementary combinations of projection images of an object. The systems and methods provide a first two-dimensional image of the object which has been obtained from a first perspective having a first spatial orientation with reference to a coordinate system. The systems and methods define a first parameter indicative of a degree to which candidate perspectives complement the first perspective, define at least one scale of values between first and second limits, the first and second limits are associated with first and second candidate perspectives having complementary and non-complementary relations to the first perspective. The systems and methods associate the values to the first parameter for the candidate perspectives and display indicia indicative of the values of the first parameter with reference to the coordinate system as guidance for selecting candidate perspectives.

Disclosed are an image processing apparatus, an image processing method and a recording medium thereof, the image processing apparatus including: a storage configured to store standard information about at least one anatomical entity; and at least one processor configured to detect regions corresponding to a plurality of anatomical entities based on a medical image obtained by scanning an object including the plurality of anatomical entities, to estimate a volume of a first anatomical entity at a predetermined point in time based on object information measured from the detected regions of the anatomical entity and the standard information stored in the storage, and to provide information about condition of the first anatomical entity based on the estimated volume. Thus, it is possible to make a diagnosis more simply and accurately by determining condition information of an anatomical entity at a point in time for the diagnosis based on a randomly taken medical image.

A method includes obtaining first spectral image data, which includes at least a first component corresponding to a targeted first K-edge based contrast agent administered to a subject if a target of the targeted first K-edge based contrast agent is present in the subject, decomposing the first spectral image data into at least the first component, reconstructing the first component thereby generating a first image of the targeted first K-edge contrast agent, determining if the targeted first K-edge contrast agent is present in the first image, and generating a signal indicating the targeted first K-edge contrast agent is present in the first image in response to determining the targeted first K-edge contrast agent is present in the first image.

The disclosure provides a method, device and a computer-readable medium for performing three-dimensional blood vessel reconstruction. The computer-implemented method includes receiving a first two-dimensional image of a blood vessel of a patient, where the first two-dimensional image is a projection image acquired in a first projection direction. The method further includes reconstructing, by a processor, a three-dimensional model of the blood vessel based on at least the first two-dimensional image. The method additional includes adjusting the three-dimensional model of the blood vessel, based on a comparison of a first optical path length determined from a second two-dimensional image of the blood vessel of the patient and a second optical path length determined from the three-dimensional model.

Systems and methods for generating one or more denoised images from a series of noisy images acquired with a medical imaging system are described. In general, the systems and methods described here implement techniques whereby the series of noisy images are formed into a spatial-temporal or spatial-spectral image matrix in which each column represents a different noisy image. The image matrix is then processed to decompose the image matrix into basis images defined by a spatial and a temporal or spectral basis. Low rank solutions are enforced and extracted from the resulting decomposed image matrix as denoised images.

A method for vascular modeling is disclosed. The method, in some embodiments, comprises receiving a plurality of 2-D angiographic images of a portion of a vasculature of a subject, and processing the images to automatically detect 2-D features, for example, paths along vascular extents, which are projected into 3-D to determine homologous features among blood vessels. In some embodiments, projection and/or image registration is iteratively altered to improve feature position matching. Based on 3-D vascular extents and their registration to 2-D images, additional features such as vascular width are optionally determined and added to the model.

An imaging system includes an imaging unit, a display unit, and at least one processor. The at least one processor is configured to acquire a first type of diagnostic imaging information of the patient; reconstruct a first image using the first type of diagnostic imaging information; if a first stop criterion for terminating imaging is not satisfied, acquire a second type of diagnostic imaging information having an increased level of acquisitional burden; reconstruct a second image; if a second stop criterion for terminating imaging is not satisfied, acquire a third type of diagnostic imaging information having an increased level of acquisitional burden, wherein the patient is maintained on a table of the imaging unit during the acquisition of the second type of diagnostic imaging information, reconstruction of the second image, and acquisition of the third type of diagnostic imaging information; reconstruct a third image; and display the third image.

The disclosure relates to a device and a method for ascertaining at least one individual fluid-dynamic characteristic parameter of a stenosis in a vascular segment having a plurality of serial stenoses, wherein angiography image data of the vascular segment is received from an angiography recording device, geometry data of the vascular segment is ascertained by an analysis device based on the angiography image data and combined into a segment model. At least one division point located between two of the stenoses respectively is ascertained by a dividing device in the segment model, the segment model is subdivided into subsegment models at each of the at least one division points, and the respective fluid-dynamic characteristic parameter is ascertained by a simulation device for at least one of the subsegment models based on respective geometry data of the subsegment model.

To acquire information relating to a vessel wall thickness by: acquiring interference signal sets of a plurality of frames including interference signal sets corresponding to a plurality of frames forming an image of the same cross section of an fundus; generating 3-D tomographic image data on the fundus from the interference signal sets of the plurality of frames; generating 3-D motion contrast data in the fundus from the interference signal sets corresponding to the plurality of frames that form the same cross section; extracting a vessel from the fundus based on the 3-D tomographic image data or the 3-D motion contrast data; detecting a coordinate of an outer surface of a vessel wall of the vessel based on the 3-D tomographic image data; and detecting a coordinate of an inner surface of the vessel wall of the vessel based on the 3-D motion contrast data.

A method if shown for obtaining improved digital radiological images during a digital subtraction angiography (DSA) intervention, carried out by the introduction of a contrast medium consisting of CO2 or other fluid in an examination zone (Z) of a human or animal body, and the creation of digital radiological images (IMC1, IMC2, IMC3 IMCn) of the examination zone (Z) at the time of contrast medium input. A mask image (IMM1, IMM2, IMM3 IMMn) of the same zone (Z) is subtracted from each image (IMC1, IMC2, IMC3 IMCn) with contrast medium and thus produce a series of “clean” digital radiological images (IMP1, IMP2, IMP3 IMPn) with limited noise. According to the method, each mask image (IMM1, IMM2, IMM3 IMMn) consists of the image with contrast (IMC (2-1), IMC (3-1) IMC (n-1)) immediately preceding the image with contrast currently obtained or by weighted combination thereof.