Koninklijke Philips Corporation N.V.

Robert Gustav Trahms

David Hope Simpson

Earl M Canfield

Vijay Thakur Shamdasani

Earl M Canfield, Ii

David N Roundhill

Pallavi Vajinepalli

Celine Firtion

Thanasis Loupas

Karthik Krishnan

Matthew Rielly

Giridhar Narasapura Rajagopalaiah

Saunak Chatterjee

There is proposed a mechanism for determining whether or not an imaging probe, such as an ultrasound imaging probe, is at a desired orientation and/or position with respect to an anatomical structure. Image data of the imaging probe is processed to generate a 3D landmark model that contains anatomical landmarks of the anatomical structure. The 3D landmark model is then processed to determine whether or not the imaging probe is at the desired orientation and/or position.

Guided acquisition of a 3D representation of an anatomical structure

An ultrasound system with a deep learning neural net feature is used to automatically identify image anatomy or pathology and the view of the anatomy seen in the image. The feature also can assess image quality in real time. Based on identified anatomy, the system can automatically annotate images, launch measurement tools and exam protocols, and perform image control adjustments to aid diagnosis and improve exam workflow.

Ultrasound system with deep learning network providing real time image identification

An ultrasound system according to some embodiments may include an ultrasound transducer configured to transmit ultrasound pulses toward tissue and generate echo signals responsive to the ultrasound pulses, a channel memory configured to store the echo signals, a beamformer configured to generated beamformed signals responsive to the echo signals, a neural network configured to receive one or more samples of the echo signals or the beamformed signals and produce a first type of ultrasound imaging data, and a processor configured to generate a second type of ultrasound imaging data, wherein the one or more processors may be further configured to generate an ultrasound image based on the first type of ultrasound imaging data and the second type of ultrasound imaging data and to cause a display communicatively coupled therewith to display the ultrasound image.

Ultrasound imaging system with a neural network for deriving imaging data and tissue information

The present disclosure describes imaging systems configured to generate index information to indicate which image frames in a plurality of image frames include one or more target anatomical features, such as a head or femur of a fetus. The confidence levels of the presence of the target anatomical features are also determined. The system may be configured to determine if the target anatomical feature is present in an image frame by implementing at least one neural network. Merit levels based on the quality of the image frames may also be determined. Measurements of the one or more items of interest may be acquired. Visual representations () of the index information (), confidence levels, merit levels, and/or measurements may be provided via a user interface. A user interface may receive user inputs based on the visual representations to navigate to specific image frames () of the plurality of image frames.

Systems and methods for frame indexing and image review

An ultrasound imaging system according to the present disclosure may include or be operatively associated with an ultrasound transducer configured to acquire echo signals responsive to ultrasound pulses transmitted toward a medium. The system may include a channel memory configured to store the acquired echo signals, a neural network coupled to the channel memory and configured to receive one or more samples of the acquired echo signals, one or more samples of beamformed signals based on the acquired echo signals, or both, and to provide imaging data based on the one or more samples of the acquired echo signals, the one or more samples of beamformed signals, or both, and may further include a display processor configured to generate an ultrasound image using the imaging data provided by the neural network. The system may further include a user interface configured to display the ultrasound image produced by the display processor.

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Edmonds, WA, United States of America

Robert Gustav Trahms