Described herein are technologies for facilitating reconstruction of flow data. In accordance with one aspect, the framework receives a four-dimensional projection image dataset and registers one or more pairs of temporally adjacent projection images in the image dataset. Two-dimensional flow maps may be determined based on the registered pairs. The framework may then sort the two-dimensional flow maps according to heart phases, and reconstruct a three-dimensional flow map based on the sorted two-dimensional flow maps.

Images that are associated with an identification of a tracking target of a patient to receive radiation treatment may be received. The images may be sorted into a sequence based on a motion of the patient. The sorted images may be provided via a graphical user interface. The sequence of the sorted images that are based on the motion of the patient may be provided.

An imaging system and method provides automated microbial growth detection for antibiotic sensitivity testing. A processing system having an image sensor for capturing images of an inoculated culture plate having antibiotic disks disposed on the culture media captures images of the plate at separate times (e.g., first and second images). The system generates pixel characteristic data for pixels of the second image from a comparison of the first image and second image. The pixel characteristic data may be indicative of plate growth. The system may access growth modeling data concerning the antibiotic disk(s) and generate simulated image data with a growth model function. The growth model function uses the growth modeling data. The simulated image data simulates growth on the plate relative to the disk(s). The system compares the simulated image and the pixel characteristic data to identify pixel region(s) of the second image that differ from the simulated image.

The present disclosure relates to a method for use in adaptive radiotherapy and a treatment planning device. The method may comprise accessing a first medical image and a second medical image that represent a region of interest of a patient at different times. Each medical image is segmented into a target region and at least one non-target region. The method may further comprise accessing a deformation vector field including a plurality of vectors, wherein each vector defines a geometric transformation to map a respective voxel in the first medical image to a corresponding voxel in the second medical image. The method may further comprise generating a modified deformation vector field by: identifying a first vector in the deformation vector field that maps a voxel in the first medical image to a voxel that is in a non-target region in the second medical image; and determining whether the first vector causes a distance between the mapped voxel and the target region to increase and, if so, reducing the magnitude of the first vector. The method may further comprise post-processing the modified deformation vector field to compensate for changes in the shape or size of the target region.

A system and method for determining patient risk stratification is provided based on body composition derived from computed tomography images using segmentation with machine learning. The system may enable real-time segmentation for facilitating clinical application of body morphological analysis sets. A fully-automated deep learning system may be used for the segmentation of skeletal muscle cross sectional area (CSA). Whole-body volumetric analysis may also be performed. The fully-automated deep segmentation model may be derived from an extended implementation of a Fully Convolutional Network with weight initialization of a pre-trained model, followed by post processing to eliminate intramuscular fat for a more accurate analysis.

One or more example embodiments of the present invention relates to a method for the automated determination of examination results in an image sequence from multiple chronologically consecutive frames, the method comprising determining diagnostic candidates in the form of contiguous image regions in the individual frames for a predefined diagnostic finding; and for a number of the diagnostic candidates, determining which candidate image regions in other frames correspond to the particular diagnostic candidate, determining whether the candidate image regions of the particular diagnostic candidate in the other frames overlap with other diagnostic candidates, generating a graph containing the determined diagnostic candidates of the frames as nodes and the determined overlaps as edges, and generating communities from nodes connected via edges.

According to one embodiment, a report creation support device includes an identifying module and a report creating module. The identifying module is configured to, when receiving an input selecting a reading order related to a first medical image in which a patient's site is captured by a first modality, specify a predetermined region of the first medical image as a region of interest, and identify a second medical image corresponding to the specified region of interest from among second medical images in which the patient's site is captured by a second modality which is different from the first modality. The report creating module is configured to attach the first medical image and the identified second medical image to a predetermined region of a reading report created for the reading order.

An image analysis method and device is for detecting failure or error in an image segmentation procedure. The method comprises comparing (14) segmentation outcomes for two or more images, representative of a particular anatomical region at different respective time points, and identifying a degree of consistency or deviation between them. Based on this derived consistency or deviation measure, a measure of accuracy of the segmentation procedure is determined (16).

The invention relates to a method of visualising a dynamic anatomical structure (1), a computer program and a user interface. The method comprises (a) providing a sequence of three-dimensional medical images (M1, M2, M3, . . . MZ) of a dynamic anatomical structure (1) spanning a time period (T), (b) providing a dynamic model (14), in particular surface of the anatomical structure, (c) determining a volume of interest (40) containing an anatomical feature of interest (3) within each of the three-dimensional images, wherein the volume of interest (40) follows the position and/or the shape of the anatomical feature of interest (3) across the time period and wherein the volume of interest (40) is smaller than the complete field of view of the three-dimensional medical images (M1, M2, M3, . . . MZ), and (d) providing a three-dimensional visualisation environment (50, 70), wherein a visualisation (45) corresponding to a particular point in time comprises (i) a volume rendering of the volume of interest (40) of the three-dimensional image; and (ii) a visualisation of the dynamic model (14) in the same coordinate system. Preferably, the three-dimensional visualisation environment (50, 70) allows for displaying the dynamic model (14) and the volume rendered volume of interest (40) for each three-dimensional image across the time period in cine mode.

A radiographic imaging apparatus includes a hardware processor that acquires rounds imaging information related to radiographic imaging of an imaging subject when making rounds, and outputs, based on the acquired rounds imaging information, decision support information supporting a decision regarding whether radiograph data generated by the radiographic imaging satisfies a prescribed image quality.