公开(公告)号：US20220317215A1

公开(公告)日：2022-10-06

申请号：US17637845

申请日：2020-08-19

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Nick FLÄSCHNER ,  Arne EWALD ,  Rudolf Mathias Johannes Nicolaas LAMERICHS

IPC: G01R33/48 ,  A61B5/378 ,  A61B5/00 ,  A61B5/375 ,  A61B5/374 ,  G01R33/54

Abstract: The present invention relates to a system (10) for functional magnetic resonance image data acquisition. The system comprises an input unit (20), a magnetic resonance imaging “MRI” device (30), an electroencephalography “EEG” data acquisition device (40), and a processing unit (50). The input unit is configured to provide task based information to a patient, wherein the task based information extends over a period of time. The MRI device is configured to acquire functional magnetic resonance imaging “fMRI” data relating to brain activity of the patient, wherein the fMRI data extends over the period of time. The EEG device is configured to acquire EEG data relating to electrical activity of the brain of the patient, wherein the EEG data extends over the period of time. The processing unit is configured to utilize the task based information that extends over the period of time and the EEG data that extends over the period of time to determine at least one first sub-set period of time over the period of time. The processing unit is configured to determine an action associated with acquisition of the fMRI data over the at least one first sub-set period of time.

公开(公告)号：US20200163645A1

公开(公告)日：2020-05-28

申请号：US16619216

申请日：2018-05-30

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Arne EWALD ,  Irina WAECHTER-STEHLE ,  Frank Michael WEBER ,  Tobias WISSEL

IPC: A61B8/08 ,  G06T7/10 ,  A61B8/00 ,  G06T7/30 ,  G06T7/00

Abstract: Provided is a method (200) for generating a combined anatomical model of a heart. The method comprises receiving (220) a non-contrast agent-enhanced ultrasound image of a left ventricular region of the heart and receiving (240) a contrast agent-enhanced ultrasound image of the left ventricular region of the heart. Image registration (260) is performed on the respective non-contrast agent-enhanced and contrast agent-enhanced ultrasound images, such that the respective images are aligned. Combined segmentation (270) of the aligned non-contrast agent-enhanced and contrast agent-enhanced ultrasound images is then carried out to generate the combined anatomical model. The combined segmentation (270) uses features of both of the aligned non-contrast agent-enhanced and contrast agent-enhanced ultrasound images as target points. Further provided is a processor arrangement adapted to implement the method and an ultrasound system comprising the processor arrangement. A computer program product comprising computer program code means adapted to implement the method is also provided.

公开(公告)号：US20220240910A1

公开(公告)日：2022-08-04

申请号：US17629465

申请日：2020-07-28

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Irina WAECHTER-STEHLE ,  Rolf Jürgen WEESE ,  Alexandra GROTH ,  Dirk SCHAEFER ,  Arne EWALD ,  Sven KROENKE

IPC: A61B17/00 ,  A61F2/24

Abstract: A system (SYS) for supporting a medical procedure, comprising an interface (IN) for receiving at least one medical input signal that describes a state of a target anatomy. A signal analyzer (SA) is configured to analyze the medical input signal to determine a time window for deployment of a cardio-vascular device (CL) to be deployed by a deployment.

公开(公告)号：US20220028133A1

公开(公告)日：2022-01-27

申请号：US17299804

申请日：2019-11-26

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Arne EWALD ,  Nick FLAESCHNER ,  Bernhard GLEICH ,  Ingmar GRAESSLIN ,  Peter BOERNERT ,  Ingo SCHMALE ,  Johannes Adrianus OVERWEG

IPC: G06T11/00 ,  G01R33/56 ,  G06N3/04 ,  G01R33/48 ,  G01R33/565

Abstract: The invention provides for a medical imaging system (100, 400) comprising a memory (110) storing machine executable instructions (120) and a configured artificial neural network (122). The medical imaging system further comprises a processor (104) configured for controlling the medical imaging system. Execution of the machine executable instructions causes the processor to receive (200) magnetic resonance imaging data (124), wherein the magnetic resonance imaging data is BOLD functional magnetic resonance imaging data descriptive of a time dependent BOLD signal (1100) for each of a set of voxels. Execution of the machine executable instructions further causes the processor to construct (202) a set of initial signals (126) by reconstructing the time dependent BOLD signal for each of the set of voxels using the magnetic resonance imaging data. Execution of the machine executable instructions further causes the processor to receive (204) a set of modified signals (128) in response to inputting the set of initial signals into the configured artificial neural network. The configured artificial neural network is configured for removing physiological artifacts from the set of initial signals.

公开(公告)号：US20220237787A1

公开(公告)日：2022-07-28

申请号：US17621718

申请日：2020-06-24

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Arne EWALD ,  Rudolf Mathias Johannes Nicolaas LAMERICHS ,  Nick FLASCHNER ,  Bernhard GLEICH ,  Peter BOERNERT ,  Ingmar GRAESSLIN ,  Johannes Adrianus OVERWEG

IPC: G06T7/00 ,  G06N3/08

Abstract: The present disclosure relates to a medical imaging method, comprising: receiving (201) a set of subject parameters descriptive of a subject; in response to inputting (203) the set of subject parameters into a trained deep neural network, DNN, receiving (205) from the trained DNN a predicted task; presenting the task to the subject; controlling (207) an MRI system (700) for acquiring fMRI data from the subject in response to the predicted task performed by the subject during the acquisition

公开(公告)号：US20220130523A1

公开(公告)日：2022-04-28

申请号：US17423179

申请日：2019-12-30

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Tobias WISSEL ,  Irina WAECHTER-STEHLE ,  Frank Michael WEBER ,  Arne EWALD

IPC: G16H30/40 ,  G06T7/136 ,  G06T7/143 ,  G06T7/174 ,  G06T7/11 ,  G06T7/00 ,  G06N20/00

Abstract: 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).

公开(公告)号：US20200293690A1

公开(公告)日：2020-09-17

申请号：US16814249

申请日：2020-03-10

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Arne EWALD ,  Tim NIELSEN ,  Karsten SOMMER ,  Irina WAECHTER-STEHLE ,  Christophe Michael Jean SCHÜLKE ,  Frank Michael WEBER ,  Rolf Jürgen WEESE ,  Jochen PETERS

IPC: G06F21/62 ,  G16H10/60 ,  G16H30/20 ,  G06N20/00

Abstract: A system (100) and computer-implemented method are provided for data collection for distributed machine learning of a machine learnable model. A privacy policy data (050) is provided defining computer-readable criteria for limiting a selection of medical image data (030) to a subset of the medical image data to obfuscate an identity of the at least one patient. The medical image data is selected based on the computer-readable criteria to obtain privacy policy-compliant training data (060) for transmission to another entity. The system and method enable medical data collection at clinical sites without requiring manual oversight, and enables such selections to be made automatically, e.g., based on a request for medical image data which may be received from outside of the clinical site.

公开(公告)号：US20200253548A1

公开(公告)日：2020-08-13

申请号：US16761194

申请日：2018-10-31

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Heiner DAERR ,  Arne EWALD

IPC: A61B5/00 ,  A61B5/11 ,  G16H50/20 ,  G06N3/08 ,  G06N20/00

Abstract: Presented are concepts for m classifying a disease or disability of a subject. One such concept comprises obtaining interaction data associated with a subject, the interaction data being representative of the subject's interaction with a movement-based input device. The interaction data is processed with a first machine learning process to determine a set of 5 characteristics for describing the subject. The set of characteristics is then processed with a second machine-learning process to generate a classification result for the subject. An instruction is provided to the subject for directing the subject to interact with the movement-based input device, wherein the instruction defines a challenge comprising a time-varying parameter.