A method for forming a virtual 3D mathematical model of a dental system, including receiving DICOM files representing the dental system; identifying number and location of voxels of tissues of the dental system; combining the voxels of the tissues into voxels of organs of the dental system; combining the organs into the virtual 3D mathematical model of the dental system, wherein the virtual 3D mathematical models supports linear, non-linear and volumetric measurements of the dental system; and presenting the virtual 3D mathematical model to a user. The DICOM files can be cone beam or multispiral computed tomography, MRT, PET and/or ultrasonography. The tissues include enamel, dentin, pulp, cartilage, periodontium, and/or jaw bone. The organs include teeth, gums, temporomandibular joint and/or jaw. A size of the voxels is typically between 40 μm and 200 μm.

An imaging planning apparatus according to one embodiment includes processing circuitry. The processing circuitry obtains a first value of a first index that is related to an X-ray dose and a second value of a second index that is related to an image quality, based on an X-ray imaging condition of a subject set in a predetermined examination. The processing circuitry displays an association chart indicating an association between the first index and the second index on a display unit, displays an acceptable range of the first index and the second index, the acceptable range being based on information related to a diagnostic reference level corresponding to the predetermined examination, in a manner distinguished from a range other than the acceptable range in the association chart, and also displays a mark at a position corresponding to the first value and the second value in the association chart.

A system and method for estimating vascular flow using CT imaging include a computer readable storage medium having stored thereon a computer program comprising instructions, which, when executed by a computer, cause the computer to acquire a first set of data comprising anatomical information of an imaging subject, the anatomical information comprises information of at least one vessel. The instructions further cause the computer to process the anatomical information to generate an image volume comprising the at least one vessel, generate hemodynamic information based on the image volume, and acquire a second set of data of the imaging subject. The computer is also caused to generate an image comprising the hemodynamic information in combination with a visualization based on the second set of data.

The apparatus for an X-ray data generation according to an embodiment of the inventive concept includes a processor that receives 3D data to generate output data and a buffer, and the processor includes an extraction unit that extracts raw object data from the 3D data and projects the raw object data onto a 2D plane to generate first object data, an augmentation unit that performs data augmentation on the first object data to generate second object data, a composition unit that synthesizes the second object data and background data to generate composite data, and a post-processing unit that performs post-processing on the composite data to generate the output data, and the buffer stores a plurality of parameters related to generation of the first object data, the second object data, the composite data, and the output data.

Disclosed herein is an X-ray apparatus for acquiring a medical image, and a method of using said X-ray apparatus, said method comprising the steps of: acquiring an original radiation image of a target object and capturing condition information of the object; acquiring a scatter radiation image related to the original radiation image by inputting the original radiation image and the capturing condition information to a learning network model configured to estimate scatter radiation; and acquiring a scatter radiation-processed medical image from the original radiation image on the basis of the original radiation image and the scatter radiation image, wherein the learning network model configured to estimate scatter radiation is a learning network model taught using a plurality of scatter radiation images and a plurality of pieces of capturing condition information related to each of the plurality of scatter radiation images.

A computer-implemented method for determining a flow rate for a given vessel includes obtaining, via a processor, dynamic three-dimensional (3D) images of a subject utilizing nuclear medicine imaging. The method also includes obtaining, via the processor, injection parameters for a radiotracer bolus injected into the subject via an automated injector. The method further includes generating, via the processor, time activity curves (TACs) for the radiotracer bolus from the 3D images. The method even further includes estimating, via the processor, the flow rate for the given vessel based on a morphology of the one or more TACs and the injection parameters.

A method for identifying a misidentified study can utilize a set of photographs captured at substantially the same time as a corresponding set of medical images. The method can include determining similarities between the photographs through machine learning models and determining that a misidentified study exists when the similarity between the photographs fails to satisfy a threshold similarity.

A non-transitory computer-readable storage medium storing a program causes a computer to perform an analysis process based on a radiation moving image in which a dynamic state of a specific site of a subject is captured. The program includes the analysis process in which, an analysis is performed based on the radiation moving image wherein when a plane in which the specific site is movable is to be a movable plane, the radiation moving image is obtained by irradiating radiation on the specific site in a state in which the radiation is orthogonal to the movable plane.

Methods and systems are disclosed for radiating a moving object. The method may comprise acquiring a plurality of indicators of the phase of a physiological cycle of a patient and a plurality of images of the patient that include a target. Each image may be taken at a different phase of the physiological cycle and may be registered to the phase at which the image was taken. The method may also include identifying the target in each of the plurality of images, calculating a dose of radiation required to treat the target, calculating the number, orientation, and dwell time of one or more radiation beams required to deliver the calculated required dose of radiation to the target, and calculating a position of each of the one or more radiation beams required to achieve the calculated orientation. Each position may be a function of the phase of the physiological cycle to which each of the plurality of images is registered.

A dynamic anatomic atlas is disclosed, comprising static atlas data describing atlas segments and dynamic atlas data comprising information on a dynamic property which information is respectively linked to the atlas segments.