# US 2021/0275047 A1 · Methods and systems for medical imaging based analysis of ejection fraction and fetal heart functions

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- Number: US 2021/0275047 A1
- Document ID: US20210275047A1
- Status: Published application
- Kind: A1
- Application number: 16/811,628
- Filed: 2020-03-06
- Published: 2021-09-09
- Earliest claimed: 2020-03-06
- Assignees: GE Precision Healthcare LLC
- Applicants: GE Precision Healthcare LLC
- Inventors: Dani Pinkovich; Sarit Shwartz; Noa Alkobi; Robert Anderson; Antonio Fabian Fermoso; Walter Duda, Jr.; Christian Fritz Perrey
- Web page: https://patentgrep.com/patent/US20210275047A1

## CPC

- A61B 5/02411: {of foetuses}
- G06T 7/12: Edge-based segmentation
- A61B 5/0044: {for the heart}
- A61B 5/02028: {Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction}
- A61B 5/344: Foetal cardiography
- A61B 5/7267: {involving training the classification device}
- A61B 8/0866: {involving foetal diagnosis; pre-natal or peri-natal diagnosis of the baby}
- A61B 8/0883: {for diagnosis of the heart}
- A61B 8/5223: {for extracting a diagnostic or physiological parameter from medical diagnostic data (for algorithms to analyse biomedical images G06T7/0012)}
- G06T 7/0012: {Biomedical image inspection}
- G06T 7/149: involving deformable models, e.g. active contour models
- G06T 7/246: using feature-based methods, e.g. the tracking of corners or segments
- A61B 8/065: {to determine blood output from the heart}
- G06T 2207/10081: Computed x-ray tomography [CT]
- G06T 2207/10088: Magnetic resonance imaging [MRI]
- G06T 2207/10132: Ultrasound image
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30044: Fetus; Embryo
- G06T 2207/30048: Heart; Cardiac
- G06T 2207/30104: Vascular flow; Blood flow; Perfusion
- G06T 2207/30241: Trajectory
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

Systems and methods are provided for enhanced heart medical imaging operations, particularly as by incorporating use of artificial intelligence (AI) based fetal heart functional analysis and/or real-time and automatic ejection fraction (EF) measurement and analysis.

## Claims

### Claim 1

A system comprising:
a medical imaging device comprising at least one processor, wherein the medical imaging device is configured to:
acquire one or more medical images during examination of a heart of a patient; and
display the one or more medical images via a display device; and
wherein the processor is configured to, in real-time during the examination of the heart:
process the one or more medical images;
automatically identify at least one structure of the heart;
automatically identify a contour of the at least one structure;
indicate the contour in the one or more medical images, during the displaying of the one or more medical image; and
provide during the indicating of the contour, a quality indicator associated with the contour.

### Claim 2 (depends on 1)

The system of claim 1, wherein the processor is configured to measure or calculate during the examination of the heart, based on processing of the one or more medical images, one or more parameters and/or indicators associated with function of the heart during the examination of the heart.

### Claim 3 (depends on 1)

The system of claim 1, wherein the processor is configured to provide during the examination of the heart, based on processing of the one or more medical images, feedback or output relating to one or more parameters and/or indicators associated with function of the heart during the examination of the heart.

### Claim 4 (depends on 1)

The system of claim 1, wherein the processor is configured to:
automatically and in real-time calculate, based on processing of the one or more medical images, ejection fraction (EF) of the heart; and
provide feedback relating to the calculated EF during the displaying of the one or more medical images.

### Claim 5 (depends on 1)

The system of claim 1, wherein the processor is configured to utilize artificial intelligence in identifying the at least one structure and/or the contour of the at least one structure.

### Claim 6 (depends on 5)

The system of claim 5, wherein the processor is configured to implement deep learning deep learning based neural networks for use in identifying the at least one structure and/or the contour of the at least one structure.

### Claim 7 (depends on 1)

The system of claim 1, wherein the processor is configured to, when automatically identify a contour of the at least one structure:
identify a plurality of contour points; and
track trajectories of plurality of contour points during cycles over one or more complete heart beat cycles.

### Claim 8 (depends on 7)

The system of claim 7, wherein the processor is configured to assess function of the heart based on tracking of the trajectories of plurality of contour points.

### Claim 9 (depends on 1)

The system of claim 1, wherein the processor is configured to adaptively control the providing quality indicator associated with the contour based on a value or a classification of the quality indicator.

### Claim 10 (depends on 9)

The system of claim 9, wherein the processor is configured to visually adjust a feedback or output corresponding to the quality indicator based on the value or the classification of the quality indicator.

### Claim 11

A non-transitory computer readable medium having stored thereon, a computer program having at least one code section, the at least one code section being executable by a machine comprising at least one processor, for causing the machine to perform one or more steps comprising:
acquiring one or more medical images during examination of a heart of a patient;
displaying the one or more medical images; and
in real-time, during the examination of the heart:
processing the one or more medical images;
automatically identifying at least one structure of the heart;
automatically identifying a contour of the at least one structure;
indicating the contour in the one or more medical images, during the displaying of the one or more medical image; and
providing during the indicating of the contour, a quality indicator associated with the contour.

### Claim 12 (depends on 11)

The non-transitory computer readable medium of claim 11, wherein the one or more steps further comprise measuring or calculating during the examination of the heart, based on processing of the one or more medical images, one or more parameters and/or indicators associated with function of the heart during the examination of the heart.

### Claim 13 (depends on 11)

The non-transitory computer readable medium of claim 11, wherein the one or more steps further comprise providing during the examination of the heart, based on processing of the one or more medical images, feedback or output relating to one or more parameters and/or indicators associated with function of the heart during the examination of the heart.

### Claim 14 (depends on 11)

The non-transitory computer readable medium of claim 11, wherein the one or more steps further comprise:
automatically and in real-time calculate, based on processing of the one or more medical images, calculating ejection fraction (EF) of the heart; and
providing feedback relating to the calculated EF during the displaying of the one or more medical images.

### Claim 15 (depends on 11)

The non-transitory computer readable medium of claim 11, wherein the one or more steps further comprise utilizing artificial intelligence in identifying the at least one structure and/or the contour of the at least one structure.

### Claim 16 (depends on 15)

The non-transitory computer readable medium of claim 15, wherein the one or more steps further comprise implementing deep learning deep learning based neural networks for use in identifying the at least one structure and/or the contour of the at least one structure.

### Claim 17 (depends on 11)

The non-transitory computer readable medium of claim 11, wherein the one or more steps further comprise, when automatically identify a contour of the at least one structure:
identifying a plurality of contour points; and
tracking trajectories of plurality of contour points during cycles over one or more complete heart beat cycles.

### Claim 18 (depends on 17)

The non-transitory computer readable medium of claim 17, wherein the one or more steps further comprise assessing function of the heart based on tracking of the trajectories of plurality of contour points.

### Claim 19 (depends on 11)

The non-transitory computer readable medium of claim 11, wherein the one or more steps further comprise adaptively controlling the providing quality indicator associated with the contour based on a value or a classification of the quality indicator.

### Claim 20 (depends on 19)

The non-transitory computer readable medium of claim 19, wherein the one or more steps further comprise visually adjusting a feedback or output corresponding to the quality indicator based on the value or the classification of the quality indicator.