Koninklijke Philips Corporation N.v.

Claudia Errico

Hua Xie

Shyam Bharat

Christine Menking Swisher

Shiying Wang

Raghavendra Srinivasa Naidu

Jochen Kruecker

William Tao Shi

Man Nguyen

Grzegorz Andrzej Toporek

Haibo Wang

Thanasis Loupas

Carolina Amador Carrascal

Paul Sheeran

Charles Tremblay-Darveau

Ramon Quido Erkamp

Balasundar Iyyavu Raju

Jeffry E Powers

Vijay Thakur Shamdasani

Gary Cheng-How Ng

Faik Can Meral

Marcin Arkadiusz Balicki

Yinhui Deng

Scott William Dianis

Elizabeth Brunelle

Qianxi Li

Seyedali Sadeghi

Samer Bou Jawde

Systems and methods for multi-level vascular imaging for construction and display of vasculature from large to small vessels and micro-vessels using a combination of varying resolution contrast enhanced ultrasound flow imaging modalities are disclosed. While one or more resolution flow imaging modes may be employed for imaging large to small vessels of a vascular tree within a large region of interest, a high resolution mode, such as super resolution imaging, constructed for delineation of the microvascular morphology and directional microcirculation is provided within one or more small ROIs placed in selected locations within the larger ROI.

Systems and methods for vascular imaging

A lung injury monitoring system () configured to monitor a patient's lungs during ventilation, comprising: an ultrasound device () configured to obtain an ultrasound image of the patient's lungs during ventilation of the patient; a processor () configured to: (i) receive the obtained ultrasound image; (ii) determine a ventilation phase of the ventilator; (iii) analyze the received ultrasound image; and (iv) determine a risk score for a potential ventilator-associated lung injury (VALI); and a user interface () configured to display the determined risk score for the potential VALI.

Methods and systems for ventilation management using lung ultrasound

Systems and methods for time-varying two-dimensional (2D) color maps for visualizing images or sequences of images are disclosed. The 2D color map may include brightness values of a hue corresponding to different intensities that changes over time. In some 2D color maps, the hue used for the intensity scale may change over time while the brightness of a pixel may not. In some 2D color maps, both the hue and the brightness may change relative to intensity over time.

Systems and methods for color mappings of contrast images

An ultrasound imaging system may analyze motion of landmarks in a temporal sequence of image frames to determine mobility of one or more landmarks. In some examples, the sequence of image frames may be analyzed by one or more artificial intelligence models to detect landmarks and generate flow fields for the detected landmarks. The flow fields may be analyzed to determine the isotropy of the movement of the landmarks. The isotropy analysis may be used to generate a quantitative mobility index.

Systems, methods, and apparatuses for quantitative assessment of organ mobility

The present disclosure describes ultrasound imaging systems and methods for ultrasonically inspecting biological tissue, such as liver and for automatically identifying and acquiring a view suitable for hepatic-renal echo-intensity ratio quantification, using one or more neural networks, which may be trained to perform image classification, segmentation, or a combination thereof to compute a confidence metric and to provide a recommendation for measurement ROIs placement on the image.

Ultrasound system with an artificial neural network for guided liver imaging

A system for obtaining medical ultrasound images of a subject comprises a probe comprising an ultrasound transducer for capturing an ultrasound image, 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 receive an ultrasound image taken by the probe, provide the image as input to a trained model, receive from the model an indication of whether the image comprises an image relevant to a medical diagnostic process, and determine whether to bookmark the image, based on the received indication.

Systems and methods for obtaining medical ultrasound images

Ultrasound image devices, systems, and methods are provided. An ultrasound imaging system comprising a processor circuit in communication with an ultrasound probe comprising a transducer array, wherein the processor circuit is configured to receive, from the ultrasound probe, a first image of a patients anatomy; detect, from the first image, a first anatomical landmark at a first location along a scanning trajectory of the patients anatomy; determine, based on the first anatomical landmark, a steering configuration for steering the ultrasound probe towards a second anatomical landmark at a second location along the scanning trajectory; and output, to a display in communication with the processor circuit, an instruction based on the steering configuration to steer the ultrasound probe towards the second anatomical landmark at the second location.

Point-of-care ultrasound (POCUS) scan assistance and associated devices, systems, and methods

Ultrasound image devices, systems, and methods are provided. An ultrasound imaging system includes a communication interface in communication with an ultrasound imaging device. The communication interface receives an ultrasound image of a subject's anatomy. The system includes a processor in communication with the communication interface. The processor applies a predictive network to the ultrasound image to identify a plurality of locations of potential malignancies in the subjects anatomy and determines a plurality of malignancy likelihoods at the plurality of locations of potential malignancies. The processor outputs, to a display in communication with the processor, the plurality of locations of potential malignancies and the plurality of malignancy likelihoods for the plurality of locations of potential malignancies to guide a biopsy determination for the subject's anatomy.

Biopsy prediction and guidance with ultrasound imaging and associated devices, systems, and methods

Ultrasound image devices, systems, and methods are provided. An ultrasound imaging system, comprising an array of acoustic elements configured to transmit ultrasound energy into an anatomy in accordance with a first preset acquisition setting, and to receive ultrasound echoes associated with the anatomy; and a processor circuit in communication with the array of acoustic elements and configured to receive, from the array, ultrasound channel data corresponding to the received ultrasound echoes; generate a first set of beamformed data by applying a predictive network to the ultrasound channel data, wherein the first set of beamformed data is associated with a second preset acquisition setting different than the first preset acquisition setting; generate an image of the anatomy from the first set of beamformed data; and output, to a display in communication with the processor circuit, the image of the anatomy.

Method and apparatus for deep learning-based ultrasound beamforming

The present disclosure describes ultrasound imaging systems and methods for ultrasonically inspecting biological tissue. An ultrasound imaging system according to the present disclosure may be configured to automatically apply tissue-specific imaging parameter settings () based upon the automatic identification of the type of tissue being scanned. Tissue type identification () may be performed automatically for the images in the live image stream () and thus adjustments to the imaging settings may be applied automatically by using a neural network () and thus dynamically during the exam obviating the need for the sonographer to manually switch presets or adjust the imaging settings when moving to different portion of the anatomy.

Ultrasound system with automated dynamic setting of imaging parameters based on organ detection

Systems and computer implemented method of generating a simulated image of a baby based on an ultrasound image of a fetus. A method comprises acquiring an ultrasound image of the fetus, and using a machine learning model to generate the simulated image of the baby, based on the ultrasound image of the fetus, wherein the simulated image of the baby comprises a prediction of how the fetus will look as a baby.

Generating a simulated image of a baby

Clinical assessment devices, systems, and methods are provided. A clinical assessment system, comprising a processor in communication with an imaging device, wherein the processor is configured to receive, from the imaging device, a sequence of image frames representative of a contrast agent perfused subjects tissue across a time period; classify the sequence of image frames into a plurality of first tissue classes and a plurality of second tissue classes based on a spatiotemporal correlation among the sequence of image frames by applying a predictive network to the sequence of image frames to produce a probability distribution for the plurality of first tissue classes and the plurality of second tissue classes; and output, to a display in communication with the processor, the probability distribution for the plurality of first tissue classes and the plurality of second tissue classes.

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Cambridge, MA, United States of America

Claudia Errico