Koninklijke Philips Corporation N.v.

Massachusetts Institute of Technology

Hua Xie

Vijay Thakur Shamdasani

Sheng-Wen Huang

Man Nguyen

Jean-Luc Francois-Marie Robert

Shiying Wang

Claudia Errico

Francois Guy Gerard Marie Vignon

William Tao Shi

Jean-Luc Robert

Keith William Johnson

Liang Zhang

Shiwei Zhou

Roy B Peterson

Anna Teresa Fernandez

John Douglas Fraser

David W Clark

Lars Jonas Olsson

Haibo Wang

Carolina Amador Carrascal

Yan Shi

Michael R Burcher

Raghavendra Srinivasa Naidu

Ji Cao

Bruce Adrian Kincy

Alexey Viktorovich Cherepakhin

Shyam Bharat

Vijay Parthasarathy

Grzegorz Andrzej Toporek

David Hope Simpson

Robert Randall Entrekin

Gary Cheng-How Ng

Christine Menking Swisher

Jean-luc Robert

Thanasis Loupas

Charles Tremblay-Darveau

Paul Sheeran

Douglas Maxwell

Jochen Kruecker

Ameet Kumar Jain

Ramon Quido Erkamp

Jeffry E Powers

Christopher Stephen Hall

Jun Seob Shin

Shriram Sethuraman

Ajay Anand

Brian W Anthony

Earl M Canfield

Seungsoo Kim

Faik Can Meral

Ying Wu

Yinhui Deng

Marcin Arkadiusz Balicki

Scott William Dianis

Earl M Canfield, Ii

Truong Huy Nguyen

Elizabeth Brunelle

Heng Yang

Qianxi Li

Doug Maxwell

Anna Teresa Ferandez

Yin Hui Deng

Systems and methods for calculating hepato-renal index (HRI) values from radiofrequency (RF) data are disclosed herein. The RF data may include fundamental frequency components, harmonic frequency components, or a combination thereof. Signal intensities within regions of interest may be calculated from the RF data. The signal intensities may be averaged to arrive at an average signal intensity value for each region of interest. In some examples, some of the highest and/or lowest signal intensity values may be removed prior to averaging. The ratio of the average signal intensities from the different regions of interest may be then be taken to arrive at the HRI values.

Systems and methods for finding hepato-renal indices

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

An ultrasound imaging system includes an a processor circuit that stores, in a memory in communication with the processor circuit, a target parameter representative of a target anatomical scan window. The processor circuit receives a first ultrasound image acquired by a first ultrasound probe with a first anatomical scan window during a first acquisition period. The processor circuit determines a first parameter representative of the first anatomical scan window. The processor circuit retrieves the target parameter from the memory. The processor circuit compares the target parameter and the first parameter. The processor circuit outputs a visual representation of the comparison to a display in communication with the processor circuit.

Ultrasound image-based identification of anatomical scan window, probe orientation, and/or patient position

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

Systems, methods, and apparatuses for confidence mapping of shear wave measurements are disclosed. Confidence maps of shear wave image measurements may be generated from one or more confidence factors. Masking of graphical overlays of tissue stiffness values, based at least in part on the confidence map is disclosed. The confidence map and/or masked graphical overlays of tissue stiffness values may be superimposed on ultrasound images and provided on a display.

Systems, methods, and apparatuses for confidence mapping of shear wave imaging

The present disclosure describes imaging systems configured to generate adaptive scanning protocols based on anatomical features and conditions identified during a prenatal scan of an object. Systems may include an ultrasound transducer configured to acquire echo signals responsive to ultrasound pulses transmitted toward a target region. Processors coupled with the transducer can generate an image frame from the echoes and provide the image frame to a first neural network. The first neural network may be configured to identify an anatomical feature in the image frame. An indication of the anatomical feature may be provided a second neural network. The second neural network may then determine an anatomical measurement to be obtained based, in part, on the feature identified. The processors may be further configured to cause an indicator of the anatomical measurement to be obtained to be displayed on a user interface.

Adaptive ultrasound scanning

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

The present disclosure describes ultrasound systems configured to perform microbubble-based contrast imaging with enhanced sensitivity. The systems can enhance echo signals derived from microbubbles while suppressing echo signals derived from tissue by detecting phase shifts exhibited by microbubbles in resonance. To detect the phase shifts, and thereby distinguish between microbubble-based signals and tissue-based signals, the systems can transmit a series of ultrasound pulses into a target region in accordance with a predefined sequence. The sequence can include an initiation pulse configured to stimulate microbubbles into nonlinear oscillation, a detection pulse configured to detect the nonlinear oscillation, and a summation pulse formed by transmitting an initiation pulse and detection pulse with a small time delay therebetween. A signal processor included in the system can determine phase shifts exhibited by the signals generated in response to the series of pulses and mask non-microbubble-based signals based on the magnitude of the detected phase shifts.

Microbubble-based contrast imaging using phase shift 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

An ultrasound system produces maps of acoustic attenuation coefficients from B mode image signals. A plurality of maps located in different parallel and elevationally separated planes (A, B, C, D, E) are produced, then compounded in the elevation direction. Confidence maps may also be produced for one or more attenuation coefficient maps, and the confidence map displayed or its measures used to determine weighting for the compounding process. The compounding of elevationally separate planes improves attenuation coefficient estimation in the presence of blood vessels affecting the estimates in one or more of the planes.

Ultrasonic imaging of acoustic attenuation coefficients with elevation compounding

An ultrasound system produces maps of acoustic attenuation coefficients from pulse echo signals. Maps are produced using different attenuation coefficient or slope estimation methods, and a plurality of maps from different estimation methods are compounded to produce a final attenuation coefficient map. Confidence maps may also be produced for one or more attenuation coefficient maps, and the confidence map displayed or its measures used to determine weighting for the compounding process.

Ultrasonic imaging of acoustic attenuation coefficients with confidence estimation

A system for visualization and quantification of ultrasound imaging data according to embodiments of the present disclosure may include a display unit, and a processor communicatively coupled to the display unit and to an ultrasound imaging apparatus for generating an image from ultrasound data representative of a bodily structure and fluid flowing within the bodily structure. The processor may be configured to estimate axial and lateral velocity components of the fluid flowing within the bodily structure, determine a plurality of flow directions within the image based on the axial and lateral velocity components, differentially encode the flow directions based on flow direction angle to generate a flow direction map, and cause the display unit to concurrently display the image including the bodily structure overlaid with the flow direction map.

Systems and methods for automatic detection and visualization of turbulent blood flow using vector flow data

The present disclosure describes ultrasound systems configured to enhance flow imaging and analysis by adaptively adjusting one or more imaging parameters in response to acquired flow measurements. Example systems can include an ultrasound transducer and one or more processors. Using the system components, mean flow velocity magnitude and acceleration can be determined within a target region during an acquisition phase, which may include a cardiac cycle. One or more adjusted flow imaging parameters, such as adjusted ensemble length, temporal smoothing filter length and/or step size, can be determined based on the acquired flow measurements to increase the signal quality of newly acquired ultrasound echo signals. The adjusted flow imaging parameters can then be applied by the ultrasound transducer during a second acquisition phase.

Adaptive ultrasound flow imaging

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

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Cambridge, MA, United States of America

Hua Xie