Koninklijke Philips Corporation N.v.

Technische Universiteit Eindhoven

Ruud Johannes Gerardus Van Sloun

Frederik Jan De Bruijn

Gerhardus Wilhelmus Lucassen

Harold Agnes Wilhelmus Schmeitz

Alphonsus Tarcisius Jozef Maria Schipper

Bart Jacob Bakker

Massimo Mischi

Wouter Marinus Benjamin Luijten

Rogier Rudolf Wildeboer

Pedro Miguel Ferreira Dos Santos Da Fonseca

Jinhui Qian

Vincent Maurice André Auvray

Sebastiaan Overeem

The invention relates to a method for generating a synthetic elastography image (), the method comprising the steps of (a) receiving a B-mode ultrasound image () of a region of interest; (b) generating a synthetic elastography image () of the region of interest by applying a trained artificial neural network () to the B-mode ultrasound image (). The invention also relates to a method for training an artificial neural network ()useful in generating synthetic elastography images, and a related computer program and system.

Method and system for generating a synthetic elastography image

The invention relates to a method for adaptive beamforming of ultrasound signals, the method comprising the steps of (a) Receiving time-aligned RF signals acquired by multiple ultrasound transducer elements in response to an ultrasound transmission; (b) Determining content-adaptive apodization weights for beamforming the time-aligned RF signals by applying a trained artificial neural network () to the time-aligned RF signals; and (c) Applying the content-adaptive apodization weights to the time-aligned RF signals to calculate a beamformed output signal. The invention also relates to a method for training an artificial neural network () useful in adaptive beamforming of ultrasound signals, and a related computer program and system.

Method and system for adaptive beamforming of ultrasound signals

Some embodiments relate to a system and method of estimating the viscoelasticity of a material. The system and method includes

Shear wave viscoelasticity imaging using local system identification

Some embodiments are directed to a method of estimating a velocity of a contrast agent. The method includes receiving a plurality of video frames that were produced using a dynamic contrast enhanced imaging process, each video frame including a plurality of pixels/voxels. Information from the video frames is used to estimate velocity vectors indicating the velocity and direction of the agent with the vascular networks. The estimated velocity can be used to diagnose cancer, such as prostate cancer. Instead of velocity vectors, agent trajectories can be determined also used for the same purpose.

Imaging of dispersion and velocity of contrast agents

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Eindhoven, Netherlands

Ruud Johannes Gerardus Van Sloun