Koninklijke Philips Corporation N.v.

Tim Philipp Harder

Thomas Buelow

Axel Saalbach

Rafael Wiemker

Hrishikesh Narayanrao Deshpande

Tom Brosch

Tanja Nordhoff

Tobias Klinder

Jens Von Berg

Stewart Young

Thomas Netsch

Karsten Sommer

Olga Starobinets

Thomas Koehler

Irina Waechter-Stehle

Eberhard Sebastian Hansis

Christopher Stephen Hall

Jochen Keupp

Fabian Wenzel

Ingmar Graesslin

Michael Gunter Helle

Alexander Schmidt-Richberg

André Goossen, Sr

Jan-Hendrik Buhk

André GOOßEN

Christopher Brian Martel

A computer implemented method of making a measurement associated with a feature of interest in an image. The method comprises using () 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 () the measurement, based on the predicted pair of points.

Making measurements in images

When performing an imaging scan on a patient, x-ray tube currents are modulated as the scan is performed. The x-ray tube current values and modulations may be recorded and accessed. The accessed values may be processed to generate overlays and displays for identifying patient and diagnostic device issues.

Systems and methods for processing and visualizing tube current modulations in medical imaging devices

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

Instant scout scan checker

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

Systems and methods for immediate image quality feedback

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 to produce a new first measurement of the first medical finding; a report generator configured to receive the new first measurement and to produce a new report based upon the new first measurement.

Guided reviews of prior exams and reports

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

Workload reducer for quality auditors in radiology

An apparatus (), for use in conjunction with a medical imaging device () having an imaging device controller () that displays a graphical user interface (GUI) () including a preview image viewport (), includes at least one electronic processor () programmed to: perform an image analysis () 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 () when the preview-derived image label information and the GUI-derived image label information are not consistent.

Real-time digital imaging and communication in medicine (DICOM) label checker

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. The image quality is used to determine a radiation dose level to be applied for generating a next pulmonary image.

System and method for assessing a pulmonary image

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.

Method for configuring a medical device

There is provided a computer-implemented method and system () for determining regions of hyperdense lung parenchyma in an image of a lung. The system () comprises a memory () comprising instruction data representing a set of instructions and a processor () configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor (), cause the processor () 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 &#x2212;800 HU.

Determining regions of hyperdense lung tissue in an image of a lung

The invention relates to a magnetic resonance imaging data processing system () for processing motion artifacts in magnetic resonance imaging data sets using a deep learning network () trained for the processing of motion artifacts in magnetic resonance imaging data sets. The magnetic resonance imaging data processing system () comprises a memory () storing machine executable instructions () and the trained deep learning network (). Furthermore, the magnetic resonance imaging data processing system () comprises a processor () for controlling the magnetic resonance imaging data processing system. Execution of the machine executable instructions () causes the processor () to control the magnetic resonance imaging data processing system () to: receive a magnetic resonance imaging data set (), apply the received magnetic resonance imaging data set () as an input to the trained deep learning network (), process one or more motion artifacts present in the received magnetic resonance imaging data set () using the trained deep learning network ().

Deep learning based processing of motion artifacts in magnetic resonance imaging data

An image processing system and related method. The system comprises an input interface (IN) configured for receiving an n[&#x2265;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.

Robust pulmonary lobe segmentation

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.

Simulating breast deformation

The present invention relates to a device and method for quality assessment of medical image datasets. To enable an automatic quality assessment of medical image datasets to identify potential errors or deviations that may be avoided in future imaging operations, the device comprises an image input () for obtaining medical image datasets together with respective acquisition information, each medical image dataset being acquired by scanning a field of view, FOV, of an examination object and the respective acquisition information representing information related to the acquisition of the respective medical image dataset; an image analysis unit () for comparing the FOV of an obtained medical image dataset with a reference FOV and for determining difference information indicating deviations of the FOV from the reference FOV; a correlation unit () for determining correlation information indicating correlations between the difference information determined for a plurality of obtained medical image datasets and the respective acquisition information; and an output unit () for issuing the determined correlation information.

Device and method for quality assessment of medical image datasets

The invention provides for a magnetic resonance imaging system () for acquiring magnetic resonance data () from an imaging zone (). The magnetic resonance imaging system comprises: a memory () for storing initial pulse sequence commands () and machine executable instructions (); and a processor () for controlling the magnetic resonance imaging system. Execution of the machine executable instructions causes the processor to receive () a set of selected pulse sequence parameters () comprising a definition of a region of interest () of a subject (). The region of interest is within the imaging zone. Execution of the machine executable instructions further causes the processor to send () an image data request to a historical database (). The image data request comprises the set of selected pulse sequence parameters. Execution of the machine executable instructions further causes the processor to receive () historical image data () from the historical database in response to the image data request. The historical database comprises multiple image data entries. Each image data entry comprises a set of historical pulse sequence parameters (). The historical database is configured to search the historical database to retrieve the historical image data by matching the set of selected pulse sequence parameters to the set of historical pulse sequence parameters. Execution of the machine executable instructions further causes the processor to display () at least a portion () of the historical image data on a user interface. Execution of the machine executable instructions further causes the processor to receive () scan input modifications () in response to displaying the at least a portion of the historical image data on the user interface. Execution of the machine executable instructions further causes the processor to generate () modified pulse sequence commands () using the initial pulse sequence commands, the set of selected pulse sequence parameters, and the scan input modifications. Execution of the machine executable instructions further causes the processor to control () the magnetic resonance imaging system to acquire the magnetic resonance data using the modified pulse sequence commands.

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Ahrensburg, Germany

Tim Philipp Harder