A dynamic image analysis apparatus includes a hardware processor. An imaging condition affects the area and the volume of a subject that are calculated from dynamic images obtained by radiographing a cyclic dynamic state of the subject from different directions. Based on a set value of the imaging condition in the radiographing, the hardware processor corrects the area or the volume of the subject calculated from the dynamic images. Further, based on (i) the volume calculated based on the corrected area or (ii) the corrected volume, the hardware processor estimates an evaluation index of a function of the subject.

A dynamic analysis apparatus includes a hardware processor. The hardware processor is configured to perform the following, calculate a prediction rate multiplied by a respiratory function value of the subject in predicting the respiratory function value when an exclusion target portion is excluded; obtain input of the exclusion target portion in an anatomical unit from the input unit, based on the anatomical unit, specify a partial region of the lung field in which a characteristic amount relating to a respiratory function in the plurality of frame images is calculated, calculate the characteristic amount related to the respiratory function in the partial region of the lung field specified from the plurality of frame images and the characteristic amount related to the respiratory function of an entire lung field, and calculate the prediction rate based on a characteristic amount ratio which is a ratio of the two calculated characteristic amounts.

A system and method for displaying images of internal anatomy includes an image processing device configured to provide high resolution images of the surgical field from low resolution scans during the procedure. The image processing device digitally manipulates a previously-obtained high resolution baseline image to produce many representative images based on permutations of movement of the baseline image. During the procedure a representative image is selected having an acceptable degree of correlation to the new low resolution image. The selected representative image and the new image are merged to provide a higher resolution image of the surgical field. The image processing device is also configured to provide interactive movement of the displayed image based on movement of the imaging device, and to permit placement of annotations on the displayed image to facilitate communication between the radiology technician and the surgeon.

A method is disclosed for performing an imaging examination of a patient via a computer tomograph. The method includes: capturing a respiratory movement of the patient; determining a respiration-correlated parameter, from the respiratory movement of the patient; specifying a measurement region of the imaging examination the measurement region including at least one z-position; automatically calculating at least one measurement parameter in accordance with the respiratory movement, using the respiration-correlated parameter as an input parameter; and performing the imaging examination of the patient, in accordance with the at least one measurement parameter in the measurement region via the computer tomograph, to capture the projection data, wherein the projection data, when captured, depicts the respiratory cycle of the patient at the at least one z-position over the complete time duration of the respiratory cycle.

A system includes a storage device storing a set of instructions and a processor in communication with the storage device. When executing the set of instructions, the processor is configured to cause the system to obtain a 3D image including a plurality of 2D images, the 3D image relating to a lesion of an object and determine a preliminary VOI in the 3D image. The processor is also configured to cause the system to determine a preliminary shape based on an intersection of the preliminary VOI and a 2D target image of the plurality of 2D images and determine a transformation matrix corresponding to an operation to transform the preliminary shape to a target shape relating to the 3D ROI. The processor is further also configured to cause the system to determine a transformed VOI based on the preliminary VOI and the transformation matrix.

A method is provided for follow-up functional imaging after obtaining a first functional image data set of a patient. The method includes obtaining a second functional image data set of the patient at a follow-up time subsequent to the obtaining of the first functional image data set. The method also includes generating a local change map using the first functional image data set and the second functional image data set. Further, the method includes generating a mutual structural similarity map using the first functional image data set and the second functional image data set. Also, the method includes generating a significant-response map using the local change map and the mutual structural similarity map. The method also includes displaying the significant-response map.

In one aspect, the present disclosure describes a method for detecting cerebrospinal fluid (CSF) flow of a subject in which magnetic resonance imaging signals of a selected region of interest of the subject's anatomy are acquired. Preferably, the selected region of interest comprises the cerebro-spinal anatomy. A central location of the selected region of interest of the subject's anatomy is determined and used to determine a mean intensity value associated with image pixels of the central location. The mean intensity value is then used to establish interior and exterior outlines of the the selected region of interest of the subject's anatomy so that the CSF flow within the interior and exterior anatomical outlines may be measured or detected.

A method and device for acquiring at least a part of an x-ray data set with at least one x-ray device. The x-ray data set includes at least one first x-ray image data set and at least one second x-ray image data set acquired separated in time that are to be evaluated together. Both x-ray image data sets show a region of interest of a patient that is subject to heart motion at different phases of a heart cycle. The second x-ray image data set is acquired triggered at a beginning of each heart cycle covered by the acquisition and with an acquisition rate such that the phases of the acquired x-ray image data set match phases of the first x-ray image data set.

In a streakline visualization apparatus, a processing unit calculates, by using an expression including a correction value for correcting an error attributable to accelerated motion of a plurality of grid points represented by position information, time differential values of velocities of fluid on the plurality of grid points at each of the plurality of first time points. The processing unit calculates, based on the velocities and the time differential values of the velocities of the fluid on the plurality of grid points at each of the plurality of first time points, positions of a series of a plurality of particles sequentially outputted from a particle generation source as analysis time progresses at each of a plurality of second time points having a second time interval shorter than the first time interval. The processing unit generates display information about a streakline indicating the series of the plurality of particles.

Frames of a video frame sequence capturing one or more skin regions of a body are provided to a first neural network. The first neural network generates respective appearance representations based on the frames. An appearance representation generated based on a particular frame is indicative of a spatial distribution of a physiological signal across the particular frame. Simultaneously with providing the frames to the first neural network, the frames are also provided to a second neural network. The second neural network determines the physiological signal based on the frames. Determining the physiological signal by the second neural network includes applying the appearance representations, generated by the first neural network, to outputs of one or more layers of the second neural network to emphasize regions, in the frames, that exhibit relatively stronger presence of the physiological signal and deemphasize regions, in the frames, that exhibit relatively weaker presence of physiological signal.