Abstract: The present invention relates to a system and a method for detecting pain experienced by a patient in relation to a pose of the patient. The system comprises a pose estimation unit configured to estimate the pose of the patient and a pain detection unit configured to detect pain experienced by the patient. The system further comprises a processing unit configured to determine a correlation between the detected pain experienced by the patient and the estimated pose of the patient. The determined correlation can be displayed on a display unit to a physician.

Abstract: A computer-implemented method of compensating for image quality issue in an image study. The method includes acquiring a current image study, measuring, via a processor, a patient positioning parameter indicating a deviation of a patient position in the current image study to an optimal patient position for an image exam type of the current image study and analyzing, via an analysis module, the current image study and the determined patient positioning parameter to determine a relationship between positioning errors and a grading of a diagnostic finding for the current image study, wherein the grading of the diagnostic finding includes one of a severity of the diagnostic finding or a certainty classification of the diagnostic finding.

Abstract: A computer implemented method of identifying changes in a subject's heart or an adjacent region over time. The method comprising: receiving a set of imaging data relating to a subject's heart that has been obtained at a plurality of points in time; generating an anatomical model of the subject's heart for each of the images in the set of imaging data so as to provide a set of anatomical models of the subject's heart corresponding to the plurality of points in time; and aligning each of the anatomical models in the set of anatomical models relative to one another so as to provide a set of aligned data of the subject's heart. The aligned data are for identifying changes in at least one region of the subject's heart by comparing the anatomical models in the set of aligned anatomical models using a machine learning model.

Abstract: The present invention relates to patient positioning. In order to facilitate patient positioning, an apparatus is provided that comprises an input unit, a processing unit, and an output unit. The input unit is configured to receive an X-ray image of an anatomy of interest of a patient obtained from a first image acquisition and a target set of pose parameters describing a target position of the anatomy of interest for image acquisition. The processing unit is configured to detect the anatomy of interest in the X-ray image, to determine a set of pose parameters describing a current position of the detected anatomy of interest in the first image acquisition, to determine a difference between the determined set of pose parameters and the target set of pose parameters, and to construct a trajectory that defines a sequence of sets of pose parameters for bringing the anatomy of interest from the current position to the target position, if the difference is equal to or greater than a pre-defined threshold.

Abstract: Technology provides baseline images for diagnostic applications, including receiving a diagnostic image relating to a condition of a patient, the diagnostic image reflecting one of a normal state or an abnormal state of the condition, and generating a baseline image via a neural network using the diagnostic image, where the neural network is trained to generate a prediction of the diagnostic image reflecting a normal state of the condition. The neural network can include a generative adversarial network (GAN) trained only on image data with a normal state of the condition, where generating the baseline image includes an optimization process to maximize a similarity between the diagnostic image and a response of the GAN. Generating the baseline image can include selecting a portion of the diagnostic image, and adjusting a relevance weighting to be applied to the selected portion of the diagnostic image in the optimization process.

Abstract: The present invention provides a retrospective analysis of subject and/or implant positioning. Provided is a method and device for assisting positioning of a subject having an implant in X-ray imaging. The method comprises the steps of receiving (S1) X-ray image data of the subject including the implant, receiving (S2) exam request data indicating at least an exam type to be performed for the specific implant, and determining at least one set of image features being assigned to the requested exam type and indicative for positioning of the subject and/or the implant relative to an X-ray imaging device. Further, the method comprises analyzing (S3) an image content of the received X-ray image data for the determined set of image features, and determining (S4), from the analysis of the image content, a feedback being indicative for positioning the subject and the X-ray imaging device to each other with respect to a target positioning, to be provided for assisting the positioning.

Abstract: The present invention relates to prospective analysis of subject and/or implant positioning. Provided is a computer-im-plemented method for assisting positioning of a subject having an orthopedic implant in X-ray imaging. The method comprises applying one or more radiofrequency, RF, signals to an anatomy of interest of the subject where the orthopedic implant is located. The method further comprises detecting a response signal and/or signal change in response to the applied RF signal impacting the orthopedic implant and tissue being adjacent to and/or surrounding the implant, and determining, from the detected response signal and/or signal change, an implant positioning estimation relative to an X-ray imaging device. Further, the method comprises determining, from the implant positioning estimation, a feedback being indicative for positioning the X-ray imaging device and the subject to each other with respect to a target positioning, to be provided for assisting the positioning.

Abstract: The present invention relates to medical imaging. In order to reduce repeat images, it is proposed to enable automated prediction of quality metrics prior to image formation by exploiting data from sensors. This may greatly improve the quality of medical image data acquired in the actual imaging examination, thereby leading to fewer retakes, less delayed treatment to patients, shortened workflow, and higher patient rate. In X-ray and CT exams, fewer retakes may also reduce radiation doses for patients.

Abstract: A method is provided for adapting a 3D field of view (FOV) in ultrasound data acquisition so as to minimize the FOV volume in a manner that is controlled and precise. The method comprises defining a volumetric region across which 3D ultrasound data is desired, and then adapting the data acquisition field of view (FOV) in dependence upon the defined volumetric region, to encompass the region. This is achieved based on adapting a scan line length (or scan depth) of each individual scan line based on the defined volumetric region. In some embodiments, the volumetric region may be defined based on anatomical segmentation of a reference ultrasound dataset acquired in an initial step, and setting the volumetric region in dependence upon boundaries of an identified object of interest. The volumetric region may in a subset of embodiments be set as the region occupied by a detected anatomical object of interest.

Abstract: A system and method for segmenting an anatomical structure in ultrasound imaging data. First ultrasound imaging data of a first region, which includes the anatomical structure, is acquired at a high resolution. Second ultrasound imaging data of a second region, which includes a region neighboring the first region, is acquired at a low resolution. The first and second ultrasound imaging data are processed to segment the anatomical structure.

Abstract: An X-ray imaging system (100) includes an X-ray source (110) and an X-ray detector (120) that are separated by an examination region (150) for performing an X-ray imaging operation on an object (160). A processor (140) is configured to identify (S120) one or more internal structures (180) within the object (160), based on a comparison of depth sensor data representing a three-dimensional surface (170) of the object (160), with an anatomical model comprising the one or more internal structures (180). The processor (140) is also configured to compute (S130), using the depth sensor data and the identified one or more internal structures (180), a surface projection (190) of the one or more internal structures, on the surface (170) of the object (160), from a perspective of the X-ray source (110); and to output (S140) an image representation of the surface projection (190) for displaying as an overlay on the surface (170) of the object (160).

Abstract: An X-ray imaging apparatus (XI) including an X-ray source (XS) with a cathode (C) and an anode (A). The source (XS) is to generate an X-radiation beam (XB). An X-ray detector (XD) detects the X-radiation after interaction with an imaged object (OB). The beam (XB) has different spectra on its anode side (AS) and cathode side (CS) caused by the heel effect when the X-ray source (XS) is in operation. -ray imaging apparatus (XI) has a heel-effect-harnessing (HH) mechanism configured to cause a pixel (PX) of the detector (XD) to be alternately exposed to both, the anode side (AS) and the cathode side (CS) of the beam (XB).

Abstract: A method and system for correcting a difference in contrast agent density in a sequence of contrast-enhanced image frames. A reference image frame is defined in the sequence of contrast-enhanced image frames, and segmentation is performed on the reference image frame to determine a location of a region of interest within the reference image frame. The region of interest is a region of the reference image frame that contains contrast agent. Other image frames in the sequence of contrast-enhanced images are corrected based on a difference in contrast agent density/image intensity in the region of interest relative to the region of interest in the reference image frame.

Abstract: A system and method for achieving more accurate results when applying an image processing task to a series of medical images of a patient, without significantly increasing processing resource. The proposed system and method is based on receiving a plurality of image sequences of a particular anatomical region, each capturing cyclical movement of an anatomical object. Each image sequence is supplied to a classifier module which employs use of one or more machine learning algorithms to derive at least one score for each image sequence indicative of predicted success or quality of a result of the image processing task if applied to the given image series. This permits an assessment to be made in advance of which of the plurality of image series is most likely to result in the best (e.g. highest quality, or greatest amount of information) results from the image processing task.

Abstract: A computer-implemented method for positioning a subject in medical imaging, comprising: receiving a first image (20) of a region of interest (14, 16) of the subject (S10); determining first positioning data based on the first image (20), wherein the first positioning data indicates an alignment of the region of interest (14, 16) relative to a first image acquisition unit used to acquire the first image (20) (S20); determining guidance data based on the first positioning data, wherein the guidance data comprises a guidance for an alignment of the region of interest (14, 16) relative to a second image acquisition unit used to acquire a second image (60) from a current alignment to a target alignment, wherein the target alignment is to correspond to that derived from the first positioning data (S30); providing the guidance data for acquiring the second image (60) (S40).

Abstract: The present disclosure provides means for improved medical image post-processing. It utilizes an image data post-processing mechanism (122) comprising an encoder (122A) configured to encode input image data with input image properties and a decoder (122B) configured to decode the input image data to provide output image data with output image properties different to the input image properties. The post-processing mechanism (122) comprises a first post-processing setting applied to the encoder and/or the decoder and assigned to first output image properties. Further, the post-processing mechanism (122) is configured to predict a second post-processing setting applicable to the encoder and/or the decoder and assigned to second output image properties. The post-processing mechanism (122) is further configured to provide an image proposal comprising the input image data post-processed with the predicted second post-processing setting.

Abstract: The present invention relates to a method and system for predicting X-ray degradation, the system comprising; a generator (10) configured to generate a deployment fingerprint data set for recording cumulative radiation exposure of a currently deployed X-ray tube; a database (20) configured to provide a training data set comprising multiple tube fingerprint data sets for recording cumulative radiation exposure of previously deployed X-ray tubes correlated with failures of the previously deployed X-ray tubes; and a neural network (30) configured to be trained using the training data set and configured to predict at least one parameter of the currently deployed X-ray tube based on the training.

Abstract: The invention provides a method for determining a confidence value for an image segmentation. The method includes obtaining an image, wherein the image comprises a view of an anatomical structure and a model of the anatomical structure is obtained, wherein the model comprises a plurality of nodes. The image is processed to generate a plurality of image segmentation outputs, wherein each image segmentation output comprises a set of values for the view, wherein each value of the set of values is associated with a node of the plurality of nodes of the model. For each node of the model, a confidence value is determined based on the plurality of values corresponding to the node. A confidence map of the anatomical structure is generated based on the confidence value of each node.

Abstract: The disclosure relates to a system for analysis of medical image data, which represents a two-dimensional or three-dimensional medical image. The system is configured to read and/or determine, for the medical image, a plurality of image quality metrics and to determine a combined quality metrics based on the image quality metrics. The system is further configured so that the determination of the combined quality metrics takes into account an interaction between the image quality metrics in their combined effect on the combined quality metrics.

Abstract: A mechanism for determining a score indicative of a success of a segmentation of a 3D image, i.e. a success score. The mechanism proposes to obtain one or more 2D images of different target views of a target object in the 3D image, by processing a segmentation result of the 3D image. (A view of) each 2D image is classified using an automated classifier. The classification results are used to determine a success score, which may indicate, for example, whether, or how closely, the 3D segmentation result represents a ground truth segmentation result with sufficient accuracy, e.g. for clinical decision making.