IP Library Granted Patent US 12,213,840
Granted Patent B2
US 12,213,840 · App. 17/693,848 · Granted Feb 4, 2025

Automatically establishing measurement location controls for doppler ultrasound

Inventors: Babajide Ayinde (Redmond, WA); Matthew Cook (Redmond, WA); Eric Wong (Redmond, WA); Alexandra Clements (Redmond, WA); Dave Willis (Redmond, WA); Pavlos Moustakidis (Redmond, WA); Vasileios Sachpekidis (Redmond, WA); Niko Pagoulatos (Redmond, WA)
Assignee: EchoNous, Inc.
A61B8/54A61B8/06A61B8/085A61B8/0883A61B8/463A61B8/469A61B8/488
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Quick Facts
Patent No.
US 12,213,840
App. No.
17/693,848
Granted
Feb 4, 2025
Kind
B2
Abstract

A facility for automatically establishing measurement location controls for Doppler ultrasound studies is described. The facility receives a first ultrasound image, and user input selecting an anatomical structure appearing in it. The facility performs localization to determine the location of the selected anatomical structure in the initial image, and determines a first placement of measurement location controls relative to the structure. On the ultrasound machine, the facility invokes one or more first Doppler ultrasound modes using the first placement. The facility receives a second ultrasound image produced by the ultrasound machine using the one or more first modes; determines a flow location and direction based on the second ultrasound image; and determines a second placement relative to the flow location. The facility invokes one or more second Doppler ultrasound modes using the second placement, and receives results from the invocation of the one or more second Doppler ultrasound modes.

Claims (50)

1. A system, comprising:

an ultrasound transducer; and

a computing device configured to directly receive ultrasound data sensed by the ultrasound transducer from a person, the received ultrasound data comprising a sequence of ultrasound images, the computing device comprising:

a processor configured to perform a method, the method comprising:

receiving a first ultrasound image from the ultrasound transducer;

receiving user input selecting an anatomical structure appearing in the first ultrasound image;

performing localization based on segmentation or key point detection of the first ultrasound image performed by:

applying a convolutional neural network to aliasing patterns in the first ultrasound image that are associated with blood flow through the person to obtain a probability map signifying a spatial location of the aliasing patterns in the first ultrasound image and the direction of the blood flow; and

determining a location of the selected anatomical structure in the first ultrasound image based on the probability map;

automatically performing a first placement of measurement location controls relative to the determined location of the selected anatomical structure;

causing the ultrasound transducer to implement one or more first Doppler ultrasound modes using the determined first measurement location control placement;

receiving one or more second ultrasound images from the ultrasound transducer using the one or more first Doppler ultrasound modes;

determining a flow location and flow direction based on the one or more second ultrasound images;

automatically performing a second placement of measurement location controls relative to the determined flow location;

causing the ultrasound transducer to implement one or more second Doppler ultrasound modes using the second measurement location control placement; and

receiving results in response to causing the ultrasound transducer to implement the one or more second Doppler ultrasound modes.

2. The system of claim 1 wherein the one or more first Doppler ultrasound modes comprise color Doppler mode,

and wherein the one or more second Doppler ultrasound modes comprise pulsed wave Doppler mode.

3. The system of claim 2 wherein the first placement of measurement location controls places a color box,

and wherein the second placement of measurement location controls places a Doppler line and a gate.

4. The system of claim 1 wherein the one or more first Doppler ultrasound modes comprise color Doppler mode,

and wherein the one or more second Doppler ultrasound modes comprise continuous wave Doppler mode.

5. The system of claim 4 wherein the first placement of measurement location controls places a color box,

and wherein the second placement of measurement location controls places a Doppler line.

6. The system of claim 1 wherein the received results comprise one or more third ultrasound images received from the ultrasound transducer while the ultrasound transducer is implementing one or more third Doppler ultrasound modes, the one or more third ultrasound images showing flow location and direction relative to anatomical structures appearing in the one or more third ultrasound images.

7. The system of claim 1 wherein the received results comprise a flow velocity determined in the implementation of the one or more second Doppler ultrasound modes.

8. An ultrasound machine, comprising:

an ultrasound transducer; and

a computing device configured to directly receive ultrasound data sensed by the ultrasound transducer from a person, the received ultrasound data comprising a sequence of ultrasound images, the computing device comprising:

a processor configured to perform a method, the method comprising:

receiving a first ultrasound image from the ultrasound transducer;

receiving user input selecting an anatomical structure appearing in the first ultrasound image;

performing localization based on aliasing patterns in the first ultrasound image that are associated with aberrant blood flow through the person to determine the location of the selected anatomical structure in the first ultrasound image;

automatically performing a first placement of measurement location controls relative to the determined location of the selected anatomical structure;

causing the ultrasound transducer to implement one or more first Doppler ultrasound modes using the determined first measurement location control placement;

receiving one or more second ultrasound images from the ultrasound transducer using the one or more first Doppler ultrasound modes;

determining a flow location and flow direction based on the one or more second ultrasound images;

automatically performing a second placement of measurement location controls relative to the determined flow location;

causing the ultrasound transducer to implement one or more second Doppler ultrasound modes using the second measurement location control placement; and

receiving results in response to causing the ultrasound transducer to implement the one or more second Doppler ultrasound modes.

9. The ultrasound machine of claim 8 wherein the one or more first Doppler ultrasound modes comprise color Doppler mode,

and wherein the one or more second Doppler ultrasound modes comprise pulsed wave Doppler mode.

10. The ultrasound machine of claim 9 wherein the first placement of measurement location controls places a color box,

and wherein the second placement of measurement location controls places a Doppler line and a gate.

11. The ultrasound machine of claim 8 wherein the one or more first Doppler ultrasound modes comprise color Doppler mode,

and wherein the one or more second Doppler ultrasound modes comprise continuous wave Doppler mode.

12. The ultrasound machine of claim 11 wherein the first placement of measurement location controls places a color box,

and wherein the second placement of measurement location controls places a Doppler line.

13. The ultrasound machine of claim 8 wherein the received results comprise one or more third ultrasound images received from the ultrasound transducer while the ultrasound transducer is implementing one or more third Doppler ultrasound modes, the one or more third ultrasound images showing flow location and direction relative to anatomical structures appearing in the one or more third ultrasound images.

14. The ultrasound machine of claim 8 wherein the received results comprise a flow velocity determined in the implementation of the one or more second Doppler ultrasound modes.

Assignments (3)
SUPPLEMENT NO. 1 TO INTELLECTUAL PROPERTY SECURITY AGREEMENT Recorded May 13, 2026
From: ECHONOUS, INC.
To: KENNEDY LEWIS INVESTMENT MANAGEMENT LLC, AS COLLATERAL AGENT
Reel/Frame 075572/0689 →
SECURITY INTEREST Recorded May 13, 2026
From: ECHONOUS, INC.
To: MS PRIVATE CREDIT ADMINISTRATIVE SERVICES LLC
Reel/Frame 074646/0670 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 14, 2022
From: AYINDE, BABAJIDE; COOK, MATTHEW; WONG, ERIC; CLEMENTS, ALEXANDRA; WILLIS, DAVE; MOUSTAKIDIS, PAVLOS; SACHPEKIDIS, VASILEIOS; PAGOULATOS, NIKO
To: ECHONOUS, INC.
Reel/Frame 059260/0213 →
Continuity (1)
Related Publication 20230285005A1 · Sep 14, 2023
References Cited (27)
US 10430946B1 · Zhou et al. · 2019 [cited by applicant]
US 10631828B1 · Hare, II et al. · 2020 [cited by applicant]
US 20140018680A1 · Guracar · 2014 [cited by examiner]
US 20140098049A1 · Koch · 2014 [cited by examiner]
US 20170360403A1 · Rothberg · 2017 [cited by examiner]
US 20180259608A1 · Golden et al. · 2018 [cited by applicant]
US 20190140596A1 · Shimamoto et al. · 2019 [cited by applicant]
US 20200054306A1 · Mehanian et al. · 2020 [cited by applicant]
US 20200155124A1 · Halmann · 2020 [cited by examiner]
US 20200260062A1 · Sharma et al. · 2020 [cited by applicant]
US 20210022716A1 · Kerby · 2021 [cited by examiner]
US 20210345992A1 · Cook et al. · 2021 [cited by applicant]
US 20210350529A1 · Ayinde et al. · 2021 [cited by applicant]
EP 3536245A1 · 2019 [cited by applicant]
WO WO2018026431A1 · 2018 [cited by applicant]
WO WO2018140596A2 · 2018 [cited by applicant]
WO WO2019201726A1 · 2019 [cited by applicant]
WO WO2020020809A1 · 2020 [cited by applicant]
American Institute of Ultrasound in Medicine, “AIUM Practice Guideline for the Performance of the Focused Assessment With Sonography for Trauma (FAST) Examination,” [cited by applicant]
Gal, Y., et al., “Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning,” Proceedings of the 33rd International Conference on Machine Learning, New York, NY, 2016, retrieved from arXiv:150… [cited by applicant]
Geifman, Y., et al., “Selective Classification for Deep Neural Networks,” Jun. 2, 2017, retrieved from arXiv:1705.08500v2, 12 pages. [cited by applicant]
International Search Report and Written Opinion, mailed Aug. 25, 2021, for International Application No. PCT/US2021/031415, 10 pages. [cited by applicant]
International Search Report and Written Opinion, mailed Feb. 24, 2022, for International Application No. PCT/US2021/058037. (11 pages). [cited by applicant]
International Search Report and Written Opinion, mailed Oct. 12, 2021, for International Application No. PCT/US2021/031193, 9 pages. [cited by applicant]
Lakshminarayanan, B., et al., “Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles,” 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, retrieved from arXiv:1612… [cited by applicant]
Liu, S. et al., “Deep learning in Medical Ultrasound Analysis: A Review,” (2019). Engineering, 5(2): 261-275. [cited by applicant]
Redmon et al., “YOLOv3: An Incremental Improvement,” Apr. 8, 2018, retrieved from arxiv.org/abs/1804.02767, 6 pages. [cited by applicant]