Methods, systems, workstations, and computer program products that provide automated implant analysis using first and second sets of patient image stacks of a patient having at least one metallic implant coupled to bone. Relevant image stack pairs are selected from the first and second patient image stacks, the image stack pairs having at least one common target object or part of a target object for analysis therein. Bone and the at least one metallic implant are segmented in the first and second image stacks to define segmented objects and/or segmented parts of objects. Selected relevant image stack pairs from the first and second patient image stacks can be registered using the selected segmented objects and/or the segmented parts of objects. Measurements of movement of the implant and/or coupled bone after the registration can be calculated using the selected segmented objects and/or the segmented parts of objects.

A method for decomposing noise into white and spatially correlated components during MR thermometry imaging includes acquiring a series of MR images of an anatomical object and generating a series of temperature difference maps of the anatomical object. The method further includes receiving a selection of a region of interest (ROI) within the temperature difference map and estimating total noise variance values depicting total noise variance in the temperature difference map. Each total noise variance value is determined using a random sampling of a pre-determined number of voxels from the ROI. A white noise component and a spatially correlated noise component of the total noise variance providing a best fit to the total noise variance values for all of the random samplings are identified. The white noise component and the spatially correlated noise component are displayed on a user interface.

A medical image display apparatus includes an image acquisition unit that receives an input of a three-dimensional brain image of a subject, a brain area division unit that divides the three-dimensional brain image of the subject into a plurality of brain areas, an image analysis unit that calculates an analysis value for each brain area from the three-dimensional brain image of the subject, a data acquisition unit that acquires information indicating a correspondence between the brain area and a function of the brain, a display unit, and a display controller that displays an image showing the brain image of the subject divided into the brain areas, a function of the brain corresponding to each of the brain areas, and the analysis value on the display unit in association with each other.

Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

An image processing method includes: obtaining DCE magnetic resonance images corresponding to a plurality of time points for a same detection target; determining average pixel grayscale values of images of a same lesion region in the DCE magnetic resonance images of the plurality of time points respectively; determining a time to peak according to the average pixel grayscale values corresponding to the plurality of time points; and generating a first-stage time-intensity image before the time to peak and a second-stage time-intensity image after the time to peak respectively according to the DCE magnetic resonance images and the time to peak. The first-stage time-intensity image and the second-stage time-intensity image are 3D images. A pixel grayscale value of each pixel in the first-stage time-intensity image and the second-stage time-intensity image represents a change rate of blood supply intensity and reflects a severity level of a lesion corresponding to the lesion region.

A method (1) is described for training a machine learning model (2) to receive as input a time-resolved three-dimensional model (4) of a heart or a portion of a heart, and to output (3) a predicted time-to-event or a measure of risk for an adverse cardiac event. The method includes receiving a training set (5). The training set (5) includes a number of time-resolved three-dimensional models (4.sub.1, . . . , 4.sub.N) of a heart or a portion of a heart. The training set (5) also includes, for each time-resolved three-dimensional model (4.sub.1, . . . , 4.sub.N), corresponding outcome data (7.sub.1, . . . , 7.sub.N) associated with the time-resolved three-dimensional model (4.sub.1, . . . , 4.sub.N). The method (1) of training a machine learning model (2) also includes, using the training set (5) as input, training the machine learning model (2) to recognise latent representations (12) of cardiac motion which are predictive of an adverse cardiac event. The method (1) of training a machine learning model (2) also includes storing the trained machine learning model (2).

A device may obtain field images of a tissue sample, apply, to the field images, spatial distortion and illumination-based corrections (including corrections for photobleaching of reagents) to derive processed field images, identify, in each processed field image, a primary area including data useful for cell or subcellular component characterization, identify, in the processed field images, areas that overlap with one another, and derive information regarding a spatial mapping of cell(s) and/or sub-cellular components of the tissue sample. Deriving the information may include performing segmentation based on the data included in the primary area of each processed field image, and obtaining flux measurements based on other data included in the overlapping areas. The device may cause the information to be loaded in a data structure to enable statistical analysis of the spatial mapping for identifying factors defining normal tissue structure, associated inflammatory or neoplastic diseases and prognoses thereof, and associated therapeutics.

An ultrasound imaging system and method includes acquiring ultrasound image data while moving an ultrasound probe, automatically identifying a plurality of segments of interest in the ultrasound image data, automatically applying temporal scaling to at least one of the plurality of segments of interest, and displaying the ultrasound image data as a panoramic view comprising a plurality of videos, where each of the plurality of videos is based on a different one of the plurality of the segments of interest, and where, based on the temporal scaling, each of the plurality of videos in the panoramic view takes the same amount of time to play.

A non-transitory recording medium storing a computer readable program that causes a computer of a moving image management apparatus, which manages a radiographic moving image, to perform: obtaining that is obtaining a first radiographic moving image and a second radiographic moving image, the first radiographic moving image showing a movement of a subject while breathing is repeated a first number of times per unit time, and the second radiographic moving image showing a movement of the subject while breathing is repeated a second number of times that is different from the first number of times per the unit time; and associating that is associating respective numbers of breaths during imagings with the first radiographic moving image and the second radiographic moving image that are obtained in the obtaining.

A method for computing lung compliance imaging, the method comprising obtaining one or more images of lungs, determining a spatial transformation of each voxel within the lungs between the lungs at an inhale position and the lungs at an exhale position to provide displacement vector estimates for each voxel within the lungs, performing volume change inference operations to determine a volume change between the lungs at the inhale position and the lungs at the exhale position based on an inhale region of interest, an exhale region of interest, and the displacement vector estimates for each voxel within the lungs, computing a lung compliance based on the volume change inference operations.