公开(公告)号：US20230030618A1

公开(公告)日：2023-02-02

申请号：US17785071

申请日：2020-12-16

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： RAFAEL WIEMKER ,  TOM BROSCH ,  HRISHIKESH NARAYANRAO DESHPANDE ,  ANDRÉ GOOSSEN ,  TIM PHILIPP HARDER ,  AXEL SAALBACH

IPC: G06T7/60 ,  G06T7/00 ,  G06T7/11

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.

公开(公告)号：US20210177296A1

公开(公告)日：2021-06-17

申请号：US16759755

申请日：2018-10-26

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： AXEL SAALBACH ,  STEFFEN WEISS ,  KARSTEN SOMMER ,  CHRISTOPHE SCHUELKE ,  MICHAEL HELLE

IPC: A61B5/055 ,  A61B5/00 ,  G16H30/40

Abstract: The invention relates to a magnetic resonance imaging system, the magnetic resonance imaging system (100) comprising: —a memory (134, 136) storing machine executable instructions (160, 162, 164), pulse sequence commands (140) and a first machine learning model (146) comprising a first deep learning network (502), wherein the pulse sequence commands (140) are configured for controlling the magnetic resonance imaging system (100) to acquire a set of magnetic resonance imaging data, wherein the first machine learning model (146) comprises a first input and a first output, —a processor, wherein an execution of the machine executable instructions (160, 162, 164) causes the processor (130) to control the magnetic resonance imaging system (100) to repeatedly perform an acquisition and analysis process comprising: —acquiring a dataset (142.1, . . . , 142.N) comprising a subset of the set of magnetic resonance imaging data from an imaging zone (108) of the magnetic resonance imaging system (100) according to the pulse sequence commands (140), —providing the dataset (142.1, . . . , 142.N) to the first input of the first machine learning model (146), I-n response of the providing, receiving a prediction (148, 502) of a motion artifact level of the acquired magnetic resonance imaging data from the first output of the first machine learning model (146), the motion artifact level characterizing a number and/or extent of motion artifacts present in the acquired magnetic resonance imaging data.20

公开(公告)号：US20200320705A1

公开(公告)日：2020-10-08

申请号：US16955959

申请日：2018-12-14

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： RAFAEL WIEMKER ,  TANJA NORDHOFF ,  THOMAS BUELOW ,  AXEL SAALBACH ,  TOBIAS KLINDER ,  TOM BROSCH ,  TIM PHILIPP HARDER ,  KARSTEN SOMMER

IPC: G06T7/00 ,  G06T7/11 ,  G06T5/00 ,  A61N5/10

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

公开(公告)号：US20170372007A1

公开(公告)日：2017-12-28

申请号：US15524322

申请日：2015-10-22

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： KONGKUO LU ,  ALEXANDRA GROTH ,  YUECHEN QIAN ,  AXEL SAALBACH ,  RANJITH NAVEEN TELLIS ,  DANIEL BYSTROV ,  RAN COHEN ,  BELA FADIDA ,  LIOR WOLLOCH

IPC: G06F19/00 ,  G06K9/46 ,  G06F17/30 ,  G06K9/66 ,  G06F17/27

CPC classification number: G06F19/321 ,  G06F16/5866 ,  G06F17/2785 ,  G06K9/4676 ,  G06K9/66

Abstract: The present application describes a system (100) and method for detecting and labeling structures of interest. The system includes a current patient study database (102) containing a current patient study (200) with clinical contextual information (706). The system also includes an image metadata processing engine (118) configured to extract metadata for preparing an input for an anatomical structure classifier (608), a natural language processing engine (120) configured to extract clinical context information (706) from the prior patient documents, an anatomical structure detection and labeling engine (718), and a display device (108) configured to display findings from the current patient study. The anatomical structure detection and labeling engine (718) is configured to identify and label one or more structures of interest (716) from the extracted metadata and clinical context information (706). The processor (112) is also configured to aggregate series level data. The method detects, label and prioritize anatomical structures (710). Specifically, once patient information is received from the current patient study (108), the labeled anatomical structures (710) and the high risk anatomical structures (714) are combined to form an optimized prioritized list of structures of interest (716).

公开(公告)号：US20170178320A1

公开(公告)日：2017-06-22

申请号：US15378112

申请日：2016-12-14

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： AXEL SAALBACH ,  DANIEL BYSTROV ,  THOMAS BUELOW ,  KONGKUO LU

IPC: G06T7/00 ,  G06F19/00 ,  G06T1/00

CPC classification number: G06T7/0012 ,  G06F19/321 ,  G06T1/0007 ,  G06T7/0014 ,  G06T2207/10004 ,  G06T2207/30168

Abstract: The present invention relates to a device, system and method for quality assessment of medical images. The device comprises an image input configured to obtain a medical image acquired according to an imaging guideline, a database access unit configured to access a database storing reference images for a plurality of imaging guidelines and for obtaining a reference image based on the imaging guideline used for acquisition of the obtained medical image, an analysis unit configured to analyze the obtained medical image in view of the obtained reference image and/or the used imaging guideline to generate quality information representing the quality of the obtained medical image, and a quality output configured to output the generated quality information.