Abstract: A mechanical ventilation device comprising at least one electronic controller configured to receive imaging data and transpulmonary pressure data associated with a lung of a patient; perform deformable image registration of the inhalation image and the exhalation image to produce a relative compliance or elasticity map of the lungs; convert the relative compliance or elasticity map of the lungs to a quantitative compliance or elasticity map of the lungs based on the inhale transpulmonary pressure and the exhale transpulmonary pressure; and display the information relating to or derived from the quantitative compliance or elasticity map on a display device.

Abstract: A respiratory monitoring device includes an electronic controller configured to: analyze an audio signal triggered during inspiratory and expiratory phases of a patient receiving mechanical ventilation therapy from a mechanical ventilator, the audio signal being acoustically coupled into the airway of the patient, to determine resonant frequencies of the airway; determine a shift in the resonant frequencies between the inspiratory and expiratory phases to determine a presence of pendelluft inside of a lung of the patient; and output an indication of the presence of pendelluft.

Abstract: A mechanical ventilation device comprises at least one electronic controller configured to: receive ultrasound data related to a thickness of a diaphragm of a patient during inspiration and expiration while the patient undergoes mechanical ventilation therapy with a mechanical ventilator; calculate a diaphragm thickness metric based on at least the ultrasound data; and when the calculated diaphragm thickness metric does not satisfy an acceptance criterion, at least one of: output an alert indicative of the calculated diaphragm thickness metric failing to satisfy the acceptance criterion; and output a recommended adjustment to one or more parameters of the mechanical ventilation therapy delivered to the patient.

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 intubation assistance device includes an electronic controller configured to: generate a patient respiratory tract geometry model of at least a portion of a human respiratory tract by inputting one or more patient variables into a statistical shape model (SSM) of at least a portion of the human respiratory tract; select a recommended endotracheal tube (ETT) size by modeling at least one ETT model inserted into the patient respiratory tract geometry model to form a virtual fit model and estimating at least one fit parameter based on the virtual fit model; and display the recommended ETT size on a display device.

Abstract: An intubation assistance device includes an electronic controller configured to: identify, from one or more images of a patient, information about the patient including at least a diameter of a trachea and a length of an intubation pathway; determine a recommended ETT size including an ETT diameter and an ETT depth of insertion from the determined diameter of the trachea and the determined length of the intubation pathway; and display the recommended ETT size on a display device.

Abstract: Iterative material decomposition of multispectral image data includes providing a plurality of spectral images of a region of interest comprising a body part and a plurality of sets of material coefficients for a plurality of materials. Each spectral image is decomposed into a plurality of material images and an offset image. At least one of the material images for each spectral image is manipulated on the basis of at least one topological constraint relating to the body part to determine for each spectral image an updated plurality of material images and an updated offset image. A plurality of spectral images is then recomposed from the corresponding updated plurality of material images and the updated offset. Intensities at image locations are compared, and the updated plurality of material images is modified. The steps are repeated until convergence, and at least one of the recomposed spectral images at convergence is output.

Abstract: The present invention relates to an apparatus (10) for presentation of dark field information. It is described to provide (210) an X-ray attenuation image of a region of interest of an object. A dark field X-ray image of the region of interest of the object is also provided (220). A plurality of sub-regions of the region of interest are defined (230) based on the X-ray attenuation image of the region of interest or based on the dark field X-ray image of the region of interest. At least one quantitative value is derived (240) for each of the plurality of sub-regions, wherein the at least one quantitative value for a sub-region comprises data derived from the X-ray attenuation image of the sub-region and data derived from the dark field X-ray image of the sub-region. A plurality of figures of merit are assigned (250) to the plurality of sub-regions, wherein a figure of merit for a sub-region is based on the at least one quantitative value for the sub-region.

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: Presented are concepts for obtaining a confidence measure for a machine learning model. One such concept process input data with the machine learning model to generate a primary result. It also generate a plurality of modified instances of the input data and processes the plurality of modified instances of the input data with the machine learning model to generate a respective plurality of secondary results. A confidence measure relating to the primary result is determined based on the secondary results.

Abstract: A three-dimensional virtual endoscopy rendering of a lumen of a tubular structure is based on both non-spectral and spectral volumetric imaging data. The three-dimensional virtual endoscopy rendering includes a 2-D image of a lumen of the tubular structure from a viewpoint of a virtual camera of a virtual endoscope passing through the lumen. In one instance, the three-dimensional virtual endoscopy rendering is similar to the view which is provided by a physical endoscopic video camera inserted into the actual tubular structure and positioned at that location. The non-spectral volumetric image data is used to determine an opacity and shading of the three-dimensional virtual endoscopy rendering. The spectral volumetric image data is used to visually encode the three-dimensional virtual endoscopy rendering to visually distinguish an inner wall of the tubular structure and structure of interest on the wall.

Abstract: The present invention relates to a calculation device (10) for comparing dark-field X-ray images. The calculation device in (10) is configured for receiving a first dark-field X-ray image (11) describing first dark-field X-ray signals of a patient at an expiration state and for receiving a second dark-field X-ray image (12) describing second dark-field X-ray signals of the patient at an inspiration state. The calculation device is further (10) configured for normalizing the first dark-field X-ray signals of the first dark-field X-ray tin image (11) with a lung thickness value describing the lung thickness at the expiration state and for normalizing the second dark-field X-ray signals of the second dark-field X-ray image (12) with a lung thickness value describing the lung thickness at the inspiration state.

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: A System for image processing (IPS), in particular for lung imaging The system (IPS) comprises an interface (IN) for receiving at least a part of a 3D image volume (VL) acquired by an imaging apparatus (IA1) of a lung (LG) of a subject (PAT) by exposing the subject (PAT) to a first interrogating signal. A layer definer (LD) of the system (IPS) is configured to define, in the 3D image volume, a layer object (LO) that includes a representation of a surface (S) of the lung (LG). A renderer (REN) of the system (IPS) is configured to render at least a part of the layer object (LO) in 3D at a rendering view (Vp) for visualization on a display device (DD).

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 method for modelling a composition of an object of interest comprises segmenting object of interest image data provided by computer tomography image data resulting in a plurality of image segments. A determined Hounsfield density value is then extracted from the object of interest image data for each image segment. A component ratio of at least two component classes is defined for the object of interest, the at least two component classes having different component Hounsfield density values. At least one component class is assigned to each image segment based on the corresponding determined Hounsfield density value resulting in simulated image segments comprising the component Hounsfield density values. The simulated image segments define simulated image data of the object of interest, where a ratio of the assigned component classes corresponds to the component ratio. A deviation between the simulated image data and the object of interest image data is then determined.

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: A system for reviewing a prior medical image and a related prior medical report, including: a plurality of measurement tools configured to: receive an input medical image and information regarding a medical finding; analyze the input medical image to determine a measurement relating to the medical finding; and output the measurement relating to the medical finding; a report analyzer configured to: receive the prior medical report; analyze the prior medical report to extract first information regarding a first medical finding described in the medical report; select a first measurement tool of the plurality of measurement tools to analyze the prior medical image based upon the first extracted information; and output the first extracted information to the first measurement tool, wherein the first measurement tool analyzes the prior medical image to produce a first updated measurement of the first medical finding and the first measurement tool analyzes a new medical image associated with the prior medical image t