The CPU 20 obtains a three-dimensional image 50 that is a captured image of an organ of a subject and obtains a plurality of ultrasound tomographic images 56 that are images of the organ successively captured at different positions. The CPU 20 identifies, for each ultrasound tomographic image 56 among the plurality of ultrasound tomographic images 56, a CT tomographic image 49 corresponding to a cross section the same as a cross section to which the ultrasound tomographic image 56 corresponds, among a plurality of CT tomographic images 49 that constitute the three-dimensional image 50, and associates position information indicating a position corresponding to the identified CT tomographic image 49 with the ultrasound tomographic image 56 among the plurality of ultrasound tomographic images 56. The CPU 20 generates a three-dimensional ultrasound image 58 from the plurality of ultrasound tomographic images 56 on the basis of the position information.

Various methods and systems are provided for stationary CT imaging. In one embodiment, a method for an imaging system includes activating a plurality of emitters of a stationary distributed x-ray source unit to emit x-ray beams toward an object within an imaging volume, where the x-ray source unit does not rotate around the imaging volume, receiving attenuated x-ray beams with one or more detector arrays to form a sparse view projection dataset, where each attenuated x-ray beam generates a different view, and reconstructing an image from the sparse view projection dataset using a sparse view reconstruction method.

The present invention relates to X-ray image data analysis of a part of a cardiovascular system of a patient in order to estimate a level of inflammation in the part of the cardiovascular system. X-ray image data is received, a segmented model of the part of the cardiovascular system is generated and predetermined features related to inflammation are extracted from the segmented model. The extracted features are used as input to an inflammation function for calculating inflammation values of which each represents a level of inflammation in the part of the cardiovascular system. The image data analysis can improve the estimation of inflammation. Furthermore, the inflammation values can be presented to a user together with suggestions for performing actions. This can for example enable a prediction of plaque development as well as future acute coronary syndrome events.

The invention provides for a medical imaging system (100, 400), comprising: The execution of the machine executable instructions (112) causes a processor (104) to: receive (200) a set of input white matter fiber tracts (118): receive (202) the label from a discriminator neural network (116) in response to inputting the set of input w hue matter fiber tracts, generate (204) an optimized feature vector (122) using the set of input white matter fiber tracts and a generator neural network ((114) if the label indicates anatomically incorrect; receive (206) the set of generated white matter fiber tracts from the generator neural network in response to inputting the optimized feature vector, and construct (208) a false positive subset (126) of the set of input white matter fiber tracts using the generated set of white matter fiber tracts.

A system, method, and apparatus for displaying a fused reconstructed image with a multidimensional image are disclosed. An example imaging system receives a selection corresponding to a portion of a displayed multidimensional visualization of a surgical site. At the selected portion of the multidimensional visualization, the imaging system displays a portion of a three-dimensional image which corresponds to the selected multidimensional visualization such that the displayed portion of the at least one of the three-dimensional image or model is fused with the displayed multidimensional visualization.

A method and a system for providing calibration for a polychromatic photon counting detector forward counting model. Measurements with multiple materials and known path lengths are used to calibrate the photon counting detector counting response of the forward model. The flux independent weighted bin response function is estimated using the expectation maximization method, and then used to estimate the pileup correction terms at plural tube voltage settings for each detector pixel. The beam hardening corrections are then applied to the measured projection data sinogram, and the corrected sinogram is reconstructed to the counting image at the selected single energy.

A measurement X-ray CT apparatus calibrates a geometrical positional relationship between a focus of an X-ray source, an X-ray detector, and a rotation center of a rotating table in advance. The measurement X-ray CT apparatus then obtains projection images by irradiating the object to be measured with X-rays to perform a CT scan, and generates a three-dimensional image of the object to be measured by CT reconstruction of the projection images. The measurement X-ray CT apparatus further includes a reference frame that is made of a material and has a structure less susceptible to environmental changes, and sensors that are located on the reference frame and intended to successively obtain calibration values of the geometrical positional relationship between the focus of the X-ray source and the X-ray detector during the CT scan. The calibration values are used as parameters of the CT reconstruction.

A method of imaging nervous tissue, comprising acquiring functional imaging modality data from a functional imaging modality which images an intrabody volume of a patient having a body part, the patient having been injected with an imaging agent having a nervous tissue uptake by an autonomic nervous system (ANS); and locating the nervous tissue in the intrabody volume based on the functional imaging modality data.

A method may include obtaining a first acquisition time period related to a scan of a first modality performed on an object. The method may also include obtaining one or more second acquisition time periods related to a scan of a second modality performed on the object. The method may also include obtaining, based on the first acquisition time period and the one or more second acquisition time periods, target data of the object acquired in the scan of the first modality. The method may also include generating one or more target images of the object based on the target data.

A novel proton imaging system incorporates positron emission detections to enhance proton therapy treatment preparation and procedural efficiencies while reducing operational costs associated with proton therapy. In one case, the novel proton imaging system incorporating a positron emission tomography (PET) module enables rapid on-the-fly in vivo range verification for proton therapy using position information from short-lived positron emitters produced during treatment. This unique in vivo range verification method produces more streamlined, accurate, and cost-effective results relative to conventional proton imaging systems. In another case, the novel proton imaging system incorporating the PET module provides a unique combinatory PET/pCT (proton computer tomography) scanning that creates more accurate maps for proton therapy planning for metabolically-active tumors. This proton imaging system also utilizes a novel concept of “virtual protons” originating from in vivo range verification measurements that mimic proton particle's characteristics for more accurate proton computer tomography (pCT) or computer tomography (CT).