# US 2021/0330285 A1 · Systems and methods for automated physiological parameter estimation from ultrasound image sequences

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- Number: US 2021/0330285 A1
- Document ID: US20210330285A1
- Status: Published application
- Kind: A1
- Application number: 17/242,064
- Filed: 2021-04-27
- Published: 2021-10-28
- Earliest claimed: 2020-04-28
- Assignees: EchoNous, Inc.
- Applicants: EchoNous, Inc.
- Inventors: Allen Lu; Babajide Ayinde
- Web page: https://patentgrep.com/patent/US20210330285A1

## CPC

- A61B 8/0883: {for diagnosis of the heart}
- A61B 8/065: {to determine blood output from the heart}
- G06T 7/0016: {involving temporal comparison}
- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- G16H 30/40: for processing medical images, e.g. editing
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G06T 2207/10132: Ultrasound image
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30048: Heart; Cardiac
- G06T 2207/30104: Vascular flow; Blood flow; Perfusion
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Systems and methods for automated physiological parameter estimation from ultrasound image sequences are provided. An ultrasound system includes an ultrasound imaging device configured to acquire a sequence of ultrasound images of a patient. An anatomical structure recognition module includes processing circuitry configured to receive the acquired sequence of ultrasound images from the ultrasound imaging device, and automatically recognize an anatomical structure in the received sequence of ultrasound images. A physiological parameters estimation module includes processing circuitry configured to automatically estimate one or more physiological parameters associated with the recognized anatomical structure.

## Claims

### Claim 1

An ultrasound system, comprising:
an ultrasound imaging device configured to acquire a sequence of ultrasound images of a patient; and
an anatomical structure recognition module including processing circuitry configured to:
receive the acquired sequence of ultrasound images from the ultrasound imaging device; and
automatically recognize an anatomical structure in the received sequence of ultrasound images; and
a physiological parameters estimation module including processing circuitry configured to automatically estimate one or more physiological parameters associated with the recognized anatomical structure.

### Claim 2 (depends on 1)

The ultrasound system of claim 1, further comprising:
an ultrasound image quality module including processing circuitry configured to determine a quality of the acquired sequence of ultrasound images.

### Claim 3 (depends on 2)

The ultrasound system of claim 2, wherein the physiological parameters estimation module is configured to automatically estimate the one or more physiological parameters based on one or more ultrasound images of the anatomical structure having a determined quality that exceeds a threshold quality level.

### Claim 4 (depends on 2)

The ultrasound system of claim 2, wherein the ultrasound image quality module is configured to determine the quality of the acquired sequence of ultrasound images based on a clarity of the ultrasound images and a view of the ultrasound images.

### Claim 5 (depends on 2)

The ultrasound system of claim 2, wherein the ultrasound image quality module is configured to determine a quality of each ultrasound image of the acquired sequence of ultrasound images.

### Claim 6 (depends on 5)

The ultrasound system of claim 5, wherein the ultrasound image quality module is configured to determine a quality of a current ultrasound image based at least partially on determined qualities of prior ultrasound images of the acquired sequence of ultrasound images.

### Claim 7 (depends on 1)

The ultrasound system of claim 1, wherein the anatomical structure recognition module is configured to automatically recognize a left ventricle of a heart, and the physiological parameters estimation module is configured to automatically estimate an ejection fraction of the heart.

### Claim 8

A system, comprising:
a machine learning model operating in machine learning circuitry, the machine learning circuitry configured to:
receive a sequence of ultrasound images of a patient;
receive prior knowledge associated with an anatomical structure; and
automatically estimate one or more physiological parameters associated with a structure depicted in the sequence of the ultrasound images of a patient based at least in part on the prior knowledge.

### Claim 9 (depends on 8)

The system of claim 8, wherein the prior knowledge includes temporal knowledge associated with the anatomical structure.

### Claim 10 (depends on 8)

The system of claim 8, wherein the prior knowledge includes spatial knowledge associated with the anatomical structure.

### Claim 11 (depends on 8)

The system of claim 8, wherein the prior knowledge includes knowledge indicative of a periodic relationship between the anatomical structure and the received sequence of ultrasound images.

### Claim 12 (depends on 11)

The system of claim 11, wherein the anatomical structure is a heart having a periodic cardiac cycle, and the machine learning circuitry is configured to automatically estimate an ejection fraction of the heart.

### Claim 13 (depends on 12)

The system of claim 12, wherein the machine learning circuitry is configured to:
select a first ultrasound image of the sequence of ultrasound images that represents an end-diastole image;
select a second ultrasound image of the sequence of ultrasound images that represents an end-systole image; and
automatically estimate the ejection fraction of the heart based on the selected first and second ultrasound images.

### Claim 14 (depends on 8)

The system of claim 8, wherein the machine learning circuitry is configured to determine a quality of the sequence of ultrasound images, and to automatically estimate the one or more physiological parameters based at least in part on the determined quality of the sequence of ultrasound images.

### Claim 15 (depends on 8)

The system of claim 8, wherein the machine learning circuitry is configured to automatically recognize the structure depicted in the sequence of ultrasound images.

### Claim 16 (depends on 15)

The system of claim 15, wherein the structure depicted in the sequence of ultrasound images is a left ventricle, and the machine learning circuitry is configured to automatically recognize the left ventricle based at least in part on the presence of one or more structures adjacent to the left ventricle in the sequence of ultrasound images.

### Claim 17

A method, comprising:
acquiring a sequence of ultrasound images of a patient;
automatically recognizing, by anatomical structure recognition circuitry, an anatomical structure in the received sequence of ultrasound images; and
automatically estimating, by physiological parameters estimation circuitry, one or more physiological parameters associated with the recognized anatomical structure.

### Claim 18 (depends on 17)

The method of claim 17, further comprising:
determining, by ultrasound image quality circuitry, a quality of the acquired sequence of ultrasound images.

### Claim 19 (depends on 17)

The method of claim 17, wherein the automatically recognizing an anatomical structure includes automatically recognizing a left ventricle of a heart, and wherein the automatically estimating one or more physiological parameters includes automatically estimating an ejection fraction of the heart.

### Claim 20 (depends on 17)

The method of claim 17, wherein the automatically estimating one or more physiological parameters includes automatically estimating at least one of a collapsibility index of an Inferior Vena Cava (IVC) or cardiac chamber dimensions.