An electronic device includes a processor configured to generate a position movement prediction field indicating prediction of a potential positional change of a branch path by a patient's biological activity for one or more branch paths based on a blood vessel image of a reference frame, correct guidewire information extracted from a blood vessel image of a target frame with respect to a catheter position of the reference frame, and select a branch path to dispose the guidewire information, among one or more branch paths of a blood vessel region based on the position movement prediction field and the corrected guidewire information; and a display configured to visualize the guidewire information on the selected branch path.

A method is for providing a medical image of a patient, acquired via a computed tomography apparatus. An embodiment of the method includes acquiring first projection data of a first measurement region; acquiring second projection data of a second measurement region; registering a reference image to the at least one respiration-correlated image of the patient, wherein the reference image corresponds to the at least one functional image of the patient or is reconstructed under a second reconstruction rule from the second projection data, to produce a deformation model; applying the deformation model to the at least one functional image of the patient; combining the at least one functional image of the patient, deformed by the applying of the deformation model, with the at least one respiration-correlated image of the patient, to produce the medical image of the patient; and providing the medical image of the patient.

Disclosed is apparatus and method for motion tracking of a subject in medical brain imaging. The method comprises providing a light projector and a first camera; projecting a first pattern sequence (S1) onto a surface region of the subject with the light projector, wherein the subject is positioned in a scanner borehole of a medical scanner, the first pattern sequence comprising a first primary pattern (P.sub.1,1) and/or a first secondary pattern (P.sub.1,2); detecting the projected first pattern sequence (S1′) with the first camera; determining a second pattern sequence (S2) comprising a second primary pattern (P.sub.2,1) based on the detected first pattern sequence (S1′); projecting the second pattern sequence (S2) onto a surface region of the subject with the light projector; detecting the projected second pattern sequence (S2′) with the first camera; and determining motion tracking parameters based on the detected second pattern sequence (S2′).

A radiation system may include a treatment assembly including a first radiation source, a second radiation source, and a first radiation detector. The first radiation source may be configured to deliver a treatment beam covering a treatment region of the radiation system, and the treatment region may be located in a bore of the radiation system. The second radiation source may be configured to deliver a first imaging beam covering a first imaging region of the radiation system, and may be mounted rotatably on a first side of the treatment assembly. The first radiation detector may be configured to detect at least a portion of the first imaging beam, and may be mounted rotatably on a second side of the treatment assembly. The treatment assembly, the second radiation source, and the first radiation detector may be positioned such that the treatment region is addressable for the radiation system.

A method of sequential monoscopic tracking is described. The method includes generating a plurality of projections of an internal target region within a body of a patient, the plurality of projections comprising projection data about a position of an internal target region of the patient. The method further includes generating external positional data about external motion of the body of the patient using one or more external sensors. The method further includes generating, by a processing device, a correlation model between the projection data and the external positional data by fitting the plurality of projections of the internal target region to the external positional data. The method further includes estimating the position of the internal target region at a later time using the correlation model.

In an embodiment of a motion correction method, a first virtual non-contrast X-ray image of a region under examination is determined based upon first spectral raw X-ray data associated with a first contrast distribution, via material decomposition. In addition, a second virtual non-contrast X-ray image of the region under examination is determined based upon second spectral raw X-ray data associated with a second contrast distribution, differing from the first contrast distribution, via material decomposition. Then the first virtual non-contrast X-ray image is registered with the second virtual non-contrast X-ray image to determine a transformation field between the two virtual non-contrast X-ray images. Finally, based upon the determined transformation field, first X-ray image data based on the first raw X-ray data is registered with second X-ray image data based on the second raw X-ray data. An X-ray imaging method, a motion correction device and an X-ray imaging system are also discussed.

For controlling reconstruction in emission tomography, the quality of data for detected emissions and/or the application controls the settings used in reconstruction. For example, a count density of the detected emissions is used to control the number of iterations in reconstruction to more likely avoid over and under fitting. The count density may be adaptively determined by re-binning through pixel size adjustment to find a smallest pixel size providing a sufficient count density. As another example, the detected data may have poor quality due to motion or high body mass index (BMI) of the patient, so the reconstruction is set to perform differently (e.g., less smoothing for high motion or a different number of iterations for high BMI). The quality of the data may be used in conjunction with the application or task for imaging the patient to control the reconstruction.

For controlling reconstruction in emission tomography, the quality of data for detected emissions and/or the application controls the settings used in reconstruction. For example, a count density of the detected emissions is used to control the number of iterations in reconstruction to more likely avoid over and under fitting. The count density may be adaptively determined by re-binning through pixel size adjustment to find a smallest pixel size providing a sufficient count density. As another example, the detected data may have poor quality due to motion or high body mass index (BMI) of the patient, so the reconstruction is set to perform differently (e.g., less smoothing for high motion or a different number of iterations for high BMI). The quality of the data may be used in conjunction with the application or task for imaging the patient to control the reconstruction.

Images can be generated with overlays indicating an amount of brain tissue damage based on the disruption of blood supply. Imaging data can be analyzed to determine perfusion parameters with respect to regions of the brain of an individual. The thresholds for the perfusion parameters with respect to the presence of damaged brain tissue can be based on a period of time elapsed since the onset of a biological condition disrupting blood flow to one or more regions of the brain of the individual. The imaging data can also be analyzed to determine measures of hypodensity with respect to regions of the brain of the individual. A likelihood of the measures of hypodensity corresponding to damaged brain tissue can also be determined based on the period of time elapsed since the onset of the biological condition.

A system and method include acquisition of a plurality of projection images of a subject, each of the projection images associated with a respective projection angle, determination, for each of the projection images, of a center-of-light location in a first image region, determination of a local fluctuation measure based on the determined center-of-light locations, and determination of a quality measure associated with the plurality of projection images based on the local fluctuation measure.