Koninklijke Philips Corporation N.v.

Faik Can Meral

Jochen Kruecker

Jean-Luc Francois-Marie Robert

Francois Guy Gerard Marie Vignon

William Tao Shi

Jun Seob Shin

Shriram Sethuraman

Sheng-Wen Huang

Shyam Bharat

Seungsoo Kim

Pingkun Yan

Ramon Quido Erkamp

Hua Xie

Man Nguyen

Grzegorz Andrzej Toporek

Claudia Errico

Shiying Wang

Carolina Amador Carrascal

Evgeniy Leyvi

Muhammad Usman Ghani

Iason Zacharias Apostolakis

Gregory Tsiang Ely

An ultrasound imaging system which acquires short and/or undersampled radiofrequency ensembles for generating color Doppler images. The ultrasound imaging system processes the short and/or undersampled ensembles to simulate color Doppler images acquired from long radiofrequency ensembles. In some examples, the ultrasound imaging system includes one or more neural networks to process the ensembles.

Systems and methods for generating color doppler images from short and undersampled ensembles

Various embodiments of the present disclosure include a thermal ablation probabilistic controller () employing an ablation probability model () trained to render a pixel ablation probability for each pixel of an ablation scan image illustrative of a static anatomical ablation. In operation, the thermal ablation probabilistic controller () spatially aligns a temporal sequence of ablation scan datasets representative of a dynamic anatomical ablation, and applies the ablation probability model () to the spatial alignment of the temporal sequence of ablation scan datasets to render the pixel ablation probability for each pixel of the ablation scan image illustrative of the static anatomical ablation.

Temporal mapping of thermal ablation areas

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

Systems and methods for encoding radiofrequency, RF, data, e.g., electrical signals, by a microbeamformer are disclosed herein. The microbeamformer may use a pseudo-random sampling pattern () to sum samples of the RF data stored in a plurality of memory cells. The memory cells may be included in a delay line of the microbeamformer in some examples. The summed samples may form an encoded signal transmitted to a decoder which reconstructs the original RF data from the encoded signal. The decoder may use knowledge of the pseudo-random sampling pattern to reconstruct the original data in some examples.

Methods and systems for encoding and decoding radio frequency data

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 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; normalize the ultrasound channel data by applying a first scaling function to the ultrasound channel data; generate beamformed data by applying a predictive network to the normalized ultrasound channel data; de-normalize the beamformed data by applying a second scaling function to the beamformed data; generate an image of the anatomy from the beamformed data; and output, to a display in communication with the processor circuit, the image of the anatomy.

Ultrasound imaging with deep learning-based beamforming and associated devices, systems, and methods

An ultrasound (US) system () includes a US scanner () and a US probe () operatively connected to the US scanner. At least one electronic processor () is programmed to: control the US scanner and US probe to acquire a series of preoperative images of a tumor and surrounding blood vessels in a region of interest (ROI) of a patient; provide a graphical user interface (GUI) () via which the acquired preoperative images are labeled with contours of the tumor and the surrounding blood vessels in the ROI; tune a trained neural network () for the patient using the labeled preoperative images to generate a patient-tuned trained neural network; perform live imaging by controlling the US scanner and US probe to acquire live images of the tumor and the surrounding blood vessels in the ROI of the patient; input the live images to the patient-tuned trained NN to output live contours of the tumor and the surrounding blood vessels; and control a display device () to display the live images with the live contours superimposed.

Automated tumor identification during surgery using machine-learning

The present disclosure describes ultrasound systems and methods configured to determine the elasticity of a target tissue. Systems can include an ultrasound transducer configured to acquire echoes responsive to ultrasound pulses transmitted toward the tissue, which may include a region of increased stiffness. Systems can also include a beamformer configured to control the transducer to transmit a push pulse into the tissue, thereby generating a shear wave in the region of increased stiffness. The beamformer can be configured to control the transducer to emit tracking pulses adjacent to the push pulse. Systems can further include a processor configured to determine a displacement amplitude of the shear wave and based on the amplitude, generate a qualitative tissue elasticity map of the tissue. The processor can combine the qualitative map with a quantitative map of the same tissue, and based on the combination, determine a boundary of the region of increased stiffness.

Shear wave amplitude reconstruction for tissue elasticity monitoring and display

An ultrasound imaging system with an inertial tracking sensor () rigidly fixed to an ultrasound probe (). In a first embodiment, a real-time pose estimation unit () enhances image based tracking using the inertial data stream to calculate out-of-plane angles of rotation and determine an out-of-plane translation by iteratively selecting planes with the estimated out-of-plane rotations with varying out-of-plane offset, computing the differences between sub-plane distances computed by speckle analysis and the selected plane minimizing for the root mean square of the differences for all selected planes. In another embodiment, the real-time pose estimation unit enhances inertial tracking using the ultrasound image data stream to estimate an in-plane rotation angle; and substituting the in-plane rotation angle for an angle of rotation estimated using the inertial data stream.

Combining image based and inertial probe tracking

In an ultrasound imaging system which produces synthetically transmit focused images, the multiline signals used to form image scanlines are analyzed for speed of sound variation, and a mapof this variation is generated. In a preferred implementation, the phase discrepancy of the received multilines caused by speed of sound variation in the medium is estimated in the angular spectrum domain for the receive angular spectrum. Once the phase is estimated for all locations in an image, the differential phase between two points at the same lateral location, but different depth, is computed. This differential phase is proportional to the local speed of sound between the two points. A color-coded two- or three-dimensional mapis produced from these speed of sound estimates and presented to the user.

Synthetic transmit focusing ultrasound system with speed of sound mapping

An ultrasound imaging system which uses multiline receive beamforming for synthetic transmit focusing are phase adjusted to account for speed of sound variation in the transmission medium. The phase discrepancy of the received multilines caused by speed of sound variation in the medium is estimated in the frequency domain for both the transmit angular spectrum and the receive angular spectrum. The phase variation is removed in the frequency domain, then an inverse Fourier transform is used to transform the frequency domain results to the spatial domain. In another implementation, the phase discrepancy of the received multilines is estimated and corrected entirely in the spatial domain.

Synthetic transmit focusing ultrasound system with speed of sound aberration correction

An ultrasonic diagnostic imaging system acquires received beams of echo signals produced in response to a plurality of transmit events. The received beams are combined with refocusing to account for differences in receive beam to transmit event locations. The delays and weights used in the refocusing are supplemented with delays and weights which correct for reverberation artifacts. The received echo signals are processed to detect the presence of reverberation artifacts and a simulated transmission of reverberation signal components to virtual point sources in the image field is calculated. This simulation produces the delays and weights used for reverberation signal compensation, or estimated reverberation signals which can be subtracted from received echo signals to reduce reverberation artifacts.

Reverberation artifact cancellation in ultrasonic diagnostic images

A system for boundary identification includes a memory () to store shear wave displacements through a medium as a displacement field including a spatial component and a temporal component. A directional filter () filters the displacement field to provide a directional displacement field. A signal processing device () is coupled to the memory to execute a boundary estimator () to estimate a tissue boundary in a displayed image based upon a history of the directional displacement field accumulated over time.

Using reflected shear waves for monitoring lesion growth in thermal ablations

A tracking system for ultrasound imaging includes an imaging probe which acquires ultrasound image information including a plurality of ultrasound image frames; an inertial measurement unit coupled to the imaging probe and which synchronously acquires tracking information including a plurality of tracking frames indicative of motion of the imaging probe; and a controller. The controller is configured to obtain the ultrasound image information for at least two of the plurality of ultrasound image frames from the plurality of ultrasound imaging frames, and determine a similarity value based upon a comparison of the at least two ultrasound image frames. The controller is configured to compute whether the similarity value (C) is less than a similarity threshold value (C), and select first or second pose estimation methods, each different from each other, based upon results of the computation of whether the similarity value (C) is less than the similarity threshold value (C).

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Mansfield, MA, United States of America

Faik Can Meral