Abstract: An apparatus (1) for use in conjunction with a medical imaging device (2) having an imaging device controller (4) that displays a graphical user interface (GUI) (8) including a preview image viewport (9). The apparatus includes at least one electronic processor (20) programmed to: receive a video feed (17) of the GUI displayed on the imaging device controller; extract a preview image (12) displayed in the preview image viewport from the live video feed of the GUI; perform an image analysis (38) on the extracted preview image to detect one or more image features (42) indicative of one or more potential problems associated with a medical imaging examination performed with the medical imaging device; and output an alert (30) when one or more potential problems associated with the medical imaging examination is detected from the one or more image features.

Abstract: A mechanism for identifying a position of one or more anatomical landmarks in a medical image. The medical image is processed with a machine-learning algorithm to generate, for each pixel/voxel of the medical image, an indicator that indicates whether or not the pixel represents part of an anatomical landmark. The indicators are then processed in turn to predict a presence and/or position of the one or more anatomical landmarks.

Abstract: A non-transitory computer readable medium (26) stores instructions readable and executable by at least one electronic processor (20) to perform a radiology report analysis method (100). The method includes: identifying occurrences of radiology findings (36) and associated uncertainty indicators (38) in a plurality of radiology reports (32); assigning uncertainty scores (40) on a numerical scale to the identified occurrences of radiology findings based on the associated uncertainty indicators; and providing a user interface (UI) (28) on a display device (24) operatively connected with the at least one electronic processor that displays a representation (44) of the uncertainty scores assigned to the occurrences of radiology findings.

Abstract: A method (100) of generating and using one or more radiology analysis tools comprising: providing a labeling user interface (28, 40) on the workstation via which a user creates a labeled dataset by defining label types; receiving a user selection of desired output as at least one of the defined label types; identifying a proposed machine learning (ML) model based on the defined label types and the desired output; providing one or more GUI dialogs (40) presenting the proposed ML model and allowing the user to generate a user-designed proposed ML model (38) from the proposed ML model; training the user-designed ML model using training data comprising at least a portion of the labeled dataset, thereby generating a trained ML model (44); and deploying the trained ML model for an analysis process applied to at least a portion of radiology images and/or radiology reports in.

Abstract: The invention relates to a system for assessing a pulmonary image which allows for an improved assessment with respect to lung nodules detectability. The pulmonary image is smoothed for providing different pulmonary images (20, 21, 22) with different degrees of smoothing, wherein signal values and noise values, which are indicative of the lung vessel detectability and the noise in these images, are determined and used for determining an image quality being indicative of the usability of the pulmonary image to be assessed for detecting lung nodules. Since a pulmonary image shows lung vessels with many different vessel sizes and with many different image values, which cover the respective ranges of potential lung nodules generally very well, the image quality determination based on the different pulmonary images with different degrees of smoothing allows for a reliable assessment of the pulmonary image's usability for detecting lung nodules.

Abstract: A non-transitory computer readable medium (26) stores instructions readable and executable by at least one electronic processor (20) to provide statistical analysis on one or more radiology databases (30, 32) in conjunction with a workstation (18) having a display device (24) and at least one user input device (22).

Abstract: The present disclosure relates to a method for configuring a medical device. The method comprises: providing a set of one or more parameters for configuring the medical device. Each parameter of the set has predefined values. A set of values of the set of parameters may be selected from the predefined values. Using the selected values the set of parameters may be set, which results in an operational configuration of the medical device. The medical device may be controlled to operate in accordance with the operational configuration, thereby an operating status of the medical device may be determined. Based on at least the operating status the operational configuration may be maintained or the selecting, setting and controlling may be repeatedly performed until a desired operating status of the medical device can be determined based on the operating statuses resulting from the controlling.

Abstract: A computer implemented method of making a measurement associated with a feature of interest in an image. The method comprises using (302) a model trained using a machine learning process to take the image as input and predict a pair of points between which to make the measurement of the feature of interest in the image. The method then comprises determining (304) the measurement, based on the predicted pair of points.

Abstract: An apparatus (1) for providing image quality feedback during a medical imaging examination includes at least one electronic processor (20) programmed to: receive a live video feed (17) of a display (6) of an imaging device controller (4) of an imaging device (2) performing the medical imaging examination; extract a preview image (12) from the live video feed; perform an image analysis (38) on the extracted preview image to determine whether the extracted preview image satisfies an alert criterion; and output an alert (30) when the extracted preview image satisfies the alert criterion as determined by the image analysis.

Abstract: Method for assessing a position of a patient with respect to an automatic exposure control chamber, AEC chamber (11, 12), for a medical exam, wherein a patient is positioned between an X-ray source and the AEC chamber (11, 12); comprising the steps:—acquiring (S10) an X-ray image (32) of at least part of the patient, wherein the AEC chamber is configured for detecting a radiation dose of the X-ray source;—determining (S20), by the control unit, a position of the AEC chamber (11, 12) with respect to the patient from the acquired X-ray image (32);—determining (S30), by the control unit, an exam protocol performed on the patient dependent on the medical exam to be performed on the patient and determining, by the control unit, an ideal position of the AEC chamber (11, 12) with respect to the patient dependent on the exam protocol, wherein the ideal position relates to a position of the patient relative to the AEC chamber (11, 12), in which the detected radiation dose is reliable for the medical exam; and—determin

Abstract: An apparatus (10) for manually auditing a set (30) of images having quality ratings (38) for an image quality metric assigned to the respective images of the set of images by an automatic quality assessment process (40) includes at least one electronic processor (20) programmed to: generate quality rating confidence values (42) indicative of confidence of the quality ratings for the respective images; select a subset (32) of the set of images for manual review based at least on the quality rating confidence values; and provide a user interface (UI) (27) via which only the subset of the set of images is presented and via which manual quality ratings (46) for the image quality metric are received for only the subset of the set of images.

Abstract: There is provided a computer-implemented method and system (100) for determining regions of hyperdense lung parenchyma in an image of a lung. The system (100) comprises a memory (106) comprising instruction data representing a set of instructions and a processor (102) configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor (102), cause the processor (102) to locate a vessel in the image, determine a density of lung parenchyma in a region of the image that neighbours the located vessel, and determine whether the region of the image comprises hyperdense lung parenchyma based on the determined density, hyperdense lung parenchyma having a density greater than ?800 HU.

Abstract: The invention relates to a magnetic resonance imaging data processing system (126) for processing motion artifacts in magnetic resonance imaging data sets using a deep learning network (146, 502, 702) trained for the processing of motion artifacts in magnetic resonance imaging data sets. The magnetic resonance imaging data processing system (126) comprises a memory (134, 136) storing machine executable instructions (161, 164) and the trained deep learning network (146, 502, 702). Furthermore, the magnetic resonance imaging data processing system (126) comprises a processor (130) for controlling the magnetic resonance imaging data processing system.

Abstract: An image processing system and related method. The system comprises an input interface (IN) configured for receiving an n[?2]-dimensional input image with a set of anchor points defined in same, said set of anchor points forming an input constellation. A constellation modifier (CM) is configured to modify said input constellation into a modified constellation. A constellation evaluator (CE) configured to evaluate said input constellation based on said hyper-surface to produce a score. A comparator (COMP) is configured to compare said score against a quality criterion. Through an output interface (OUT) said constellation is output if the score meets said criterion. The constellation suitable to define a segmentation for said input image.

Abstract: A system and a method are provided for simulating a breast deformation of a subject using sensor data obtained from an orientation sensor of a mobile device, with the orientation sensor being configured for sensing an orientation of the mobile device with respect to a direction of gravity. In accordance with the system and method, model data is accessed which defines a biomechanical model of a breast. A simulation subsystem is provided for obtaining the sensor data from the orientation sensor of the mobile device and for determining a gravitational breast deformation by applying a gravitational force to the biomechanical model in a direction which is defined based on the orientation of the mobile device. A deformed model may be displayed on a display of the mobile device. The system and the method provide an intuitive way of simulating the breast deformation.

Abstract: An apparatus (1), for use in conjunction with a medical imaging device (2) having an imaging device controller (4) that displays a graphical user interface (GUI) (8) including a preview image viewport (9), includes at least one electronic processor (20) programmed to: perform an image analysis (38) on a preview image displayed in the preview image viewport to generate preview-derived image label information; extract GUI-derived image label information from the GUI excluding the preview image displayed in the preview image viewport; and output an alert (30) when the preview-derived image label information and the GUI-derived image label information are not consistent.

Abstract: This application proposes an improved medical imaging device enabling a timely communication of critical findings. The medical imaging device comprises an image acquisition unit, adapted to acquire image data of a subject to be imaged. The medical imaging device further comprises a local data processing device having an artificial-intelligence-module, Al-module, adapted to automatically detect a finding on basis of the acquired image data and to determine a priority status of the detected finding. Further, the medical imaging device comprises a notification module, adapted to provide, if the determined priority status reaches or exceeds a notification threshold, a notification data containing the detected finding. The application further proposes a medical imaging system, a method of operating a medical imaging device, a computer program element and a computer-readable medium having stored the computer program element.

Abstract: An image processing system and related method. The system comprises an input interface (IN) configured for receiving an n[?2]-dimensional input image with a set of anchor points defined in same, said set of anchor points forming an input constellation. A constellation modifier (CM) is configured to modify said input constellation into a modified constellation. A constellation evaluator (CE) configured to evaluate said input constellation based on said hyper-surface to produce a score. A comparator (COMP) is configured to compare said score against a quality criterion. Through an output interface (OUT) said constellation is output if the score meets said criterion. The constellation suitable to define a segmentation for said input image.

Abstract: The invention relates to a magnetic resonance imaging data processing system (126) for processing motion artifacts in magnetic resonance imaging data sets using a deep learning network (146, 502, 702) trained for the processing of motion artifacts in magnetic resonance imaging data sets. The magnetic resonance imaging data processing system (126) comprises a memory (134, 136) storing machine executable instructions (161, 164) and the trained deep learning network (146, 502, 702). Furthermore, the magnetic resonance imaging data processing system (126) comprises a processor (130) for controlling the magnetic resonance imaging data processing system.

Abstract: The present invention relates to a device and method for quality assessment of medical image datasets.