Method and apparatus for recognizing moving anatomical structures using ultrasound

Patent metadata

Field	Value
Publication number	US-9636081-B2
Application number	US-201013376968-A
Country	US
Kind code	B2
Filing date	Jun 7, 2010
Priority date	Jun 9, 2009
Publication date	May 2, 2017
Grant date	May 2, 2017

Abstract

A method of recognizing at least one moving anatomical structure using ultrasound data that operates by receiving ultrasound data ( 100 ). The ultrasound data comprises Doppler shift information which provides information descriptive of the velocity of at least one anatomical structure. The ultrasound data is first divided into a series of time frames ( 102 ). A classification is then assigned to each of the time frames using the Doppler shift information ( 104 ). The at least one anatomical structure is then recognized by using the classification of each time frame ( 106 ). This is possible, because different anatomical structures produce different patterns in the Doppler shift information.

First claim

The invention claimed is:

1. A method of recognizing at least one moving fetal anatomical structure using ultrasound data, the method comprising:
receiving, by a processor of a fetal monitor, the ultrasound data, wherein the ultrasound data comprises Doppler shift information descriptive of the velocity of the at least one moving fetal anatomical structure,
dividing, by the processor, the ultrasound data into a series of time frames, determining a feature vector for each of the time frames, wherein each feature vector represents a component of the at least one moving fetal anatomical structure,
assigning, by the processor, each of the time frames a classification using the Doppler shift information, the classification being assigned using a pattern recognition module of the processor that determines the classification based on the respective feature vector of the time frame being assigned, the classification being indicative of a movement state of each of the at least one moving fetal anatomical structure, and
recognizing, by the pattern recognition module of the processor, the at least one moving fetal anatomical structure using patterns derived from the classification of each time frame.

2. The method of claim 1 , wherein the assigning each of the time frames a classification using the Doppler shift information comprises:
identifying fetal heart valve motion data using the Doppler shift information,
identifying fetal heart wall motion data using the Doppler shift information, and
wherein a fetal heart is recognized as the at least one moving fetal anatomical structure.

3. The method of claim 1 , wherein assigning each of the time frames a classification using the Doppler shift information further comprises:
identifying fetal body motion using the Doppler shift information, and wherein the at least one moving fetal anatomical structure is identified to be a fetal body using the classification of each of the time frames.

4. The method of claim 1 , wherein the pattern recognition module is further adapted for recognizing different types of fetal body motion using the feature vector.

5. The method claim 1 , wherein the method further comprises:
receiving a measurement from a labor contraction sensor and constructing each feature vector using the measurement from the labor contraction sensor,
wherein the method further comprises receiving a phono cardiography measurement from a microphone and constructing each feature vector using the phono cardiography measurement from the microphone,
wherein the method further comprises receiving a measurement from a electrocardiography system and constructs each feature vector using the measurement from the electrocardiography system,
wherein the method further comprises receiving a measurement from a pulse oximetry system and constructs each feature vector using the measurement from the pulse oximetry system,
wherein the method further comprises receiving a measurement from a saturation of peripheral oxygen system and constructs each feature vector using the measurement from the saturation of the peripheral oxygen system,
wherein the method further comprises receiving a measurement from a non-invasive blood pressure system and constructs each feature vector using the measurement from the non-invasive blood pressure system.

6. The method of claim 1 , wherein the pattern recognition module is an implementation of a hidden Markov model.

7. A non-transitory computer-readable medium carrying software comprising machine executable instructions for execution by a control system, the machine executable instructions comprising the steps of:
receiving ultrasound data, wherein the ultrasound data comprises Doppler shift information descriptive of the velocity of at least one moving fetal anatomical structure,
dividing the ultrasound data into a series of time frames,
determining a feature vector for each of the time frames, wherein each feature vector represents a component of the at least one moving fetal anatomical structure,
assigning each of the time frames a classification using the Doppler shift information, the classification being assigned using a pattern recognition module that determines the classification based on the respective feature vector of the time frame being assigned, the classification being indicative of a movement state of each of the at least one moving fetal anatomical structure, and
recognizing the at least one moving fetal anatomical structure using patterns derived from the classification of each time frame.

8. A control system, comprising:
a microprocessor configured to perform the steps of:
receiving ultrasound data, wherein the ultrasound data comprises Doppler shift information descriptive of the velocity of at least one moving fetal anatomical structure,
dividing the ultrasound data into a series of time frames,
determining a feature vector for each of the time frames, wherein each feature vector represents a component of the at least one moving fetal anatomical structure,
assigning each of the time frames a classification using the Doppler shift information, the classification being assigned using a pattern recognition module that determines the classification based on the respective feature vector of the time frame being assigned, the classification being indicative of a movement state of each of the at least one moving fetal anatomical structure, and
recognizing the at least one moving fetal anatomical structure using patterns derived from the classification of each time frame; and
a memory arrangement configured to store the ultrasound data.

9. The control system of claim 8 , wherein assigning each time frame a classification using the Doppler shift information further comprises the steps of:
identifying fetal heart valve motion data using the Doppler shift information,
identifying fetal heart wall motion data using the Doppler shift information, and
wherein a fetal heart is recognized as the at least one moving fetal anatomical structure using the classification.

10. The control system of claim 9 ,
wherein the fetal heart valve motion data is identified using a high pass filter on the ultrasound data,
wherein the fetal heart wall motion data is identified using a low pass filter,
wherein the high pass filter uses a cutoff frequency between 200 Hz and 400 Hz,
and
wherein the low pass filter uses a cutoff frequency between 200 Hz and 400 Hz.

11. The control system of claim 10 , wherein the high pass filter uses a cutoff frequency between 250 Hz and 350 Hz and wherein the low pass filter uses a cutoff frequency between 250 Hz and 350 Hz.

12. The control system of claim 9 , wherein assigning each time frame a classification using the Doppler shift information comprises the steps of:
identifying fetal body motion data using the Doppler shift information, and wherein the at least one moving fetal anatomical structure is identified to be a fetal body using the classification of each of the time frames, and
wherein the fetal body motion data is identified using a low pass filter with a cutoff frequency between 1 Hz and 15 Hz.

13. The control system of claim 12 , wherein the cutoff frequency is between 8 Hz and 12 Hz.

14. The control system of claim 8 further comprising an ultrasound system adapted for measuring Doppler shifted ultrasound signals using an ultrasonic transducer, wherein the ultrasound system is adapted for generating the ultrasound data using the Doppler shifted ultrasound signals.

15. The control system of claim 14 , further comprising a labor contraction sensor, wherein the microprocessor is furthe

Assignees

• Reuter Stefan
• Dubielczyk Alexander
• Wohlschlager Markus
• Koninklijke Philips Nv

Inventors

• Reuter Stefan
• Dubielczyk Alexander
• Wohlschlager Markus

Classifications

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• A61B5/02411

  of foetuses · CPC title

• A61B8/02 Primary

  Measuring pulse or heart rate · CPC title

• A61B5/4362

  Assessing foetal parameters · CPC title

• A61B8/0866

  involving foetal diagnosis; pre-natal or peri-natal diagnosis of the baby · CPC title