SYSTEM AND METHOD FOR ESTIMATING ARTERIAL PULSE WAVE VELOCITY

• US 20170132788A1
• Filed: 11/10/2015
• Published: 05/11/2017
• Est. Priority Date: 11/10/2015
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Foo, Thomas Kwok-Fah, Hardy, Christopher Judson, Tan, Ek Tsoon, Venugopal, Prem
• Granted Patent: US 10,438,355 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/022 by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/0285 Measuring or recording phase velocity of blood waves
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/30104 Vascular flow; Blood flow; Perfusion
• CPC Class Codes: G06T 7/0014 using an image reference approach
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/62 of area, perimeter, diameter or volume
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A method for determining an arterial pulse wave velocity representative of a health condition of a blood vessel includes receiving an image data set comprising a plurality of images of a subject, from an imaging modality. The method also involves determining a blood vessel region in an image from the plurality of images. The method further includes determining a plurality of cross-sectional area values of a blood vessel at a plurality of locations in the blood vessel region, corresponding to a plurality of phases of a cardiac cycle of the subject and determining a plurality of flow rate values of blood flowing in the blood vessel corresponding to the plurality of cross-sectional area values. The method also includes determining a hemodynamic model based on the plurality of cross-sectional area values and the plurality of blood flow rate values and determining the arterial pulse wave velocity based on the hemodynamic model.

Claims

1. A method comprising:

receiving an image data set comprising a plurality of images of a subject, from an imaging modality;

determining a blood vessel region in an image from the plurality of images, using an image segmentation technique;

determining a plurality of cross-sectional area values of a blood vessel at a plurality of locations in the blood vessel region, corresponding to a plurality of phases of a cardiac cycle of the subject;

determining a plurality of flow rate values of blood flowing in the blood vessel corresponding to the plurality of cross-sectional area values, based on the image data set;

determining a hemodynamic model based on the plurality of cross-sectional area values and the plurality of blood flow rate values; and

determining an arterial pulse wave velocity based on the hemodynamic model, wherein the arterial pulse wave velocity is representative of a health condition of the blood vessel.

2. The method of claim 1, wherein receiving an image data set comprises acquiring a magnetic resonance image data set from a magnetic resonance imaging modality, using a velocity-phase encoding gradient field.

3. The method of claim 1, wherein determining a hemodynamic model comprises determining a one dimensional pulse propagation model and a lumped resistance-capacitance-resistance (RCR) model coupled to the one dimensional pulse propagation model.

4. The method of claim 1, wherein the hemodynamic model comprises a three dimensional computation fluid dynamic model and a lumped resistance-capacitance-resistance (RCR) model coupled to the three dimensional computation fluid dynamic model.

5. The method of claim 1, further comprising determining a mean blood pressure based on a plurality of cuff measurements of the subject.

6. The method of claim 1, wherein determining a hemodynamic model comprises determining at least one of a mean blood pressure, a blood vessel compliance value, and a lumped model compliance value based on an optimization technique.

7. The method of claim 6, wherein the optimization technique is based on a genetic algorithm.

8. The method of claim 6, wherein the optimization technique is based on a gradient descent technique.

9. The method of claim 6, wherein the optimization technique comprises using a cost function based on a difference between a plurality of predicted values obtained from the hemodynamic model and a plurality of corresponding measured values obtained from the imaging data set.

10. The method of claim 9, wherein the corresponding measured values are the cross-sectional area values of the blood vessel.

11. The method of claim 9, wherein the corresponding measured values are the blood flow rate values of the blood vessel.

12. A system comprising:

an imaging modality;

a processor module coupled to the imaging modality and configured to;

receive an image data set comprising a plurality of images of a subject, from the imaging modality;

determine a blood vessel region in an image from the plurality of images, using an image segmentation technique;

determine a plurality of cross-sectional area values of a blood vessel, at a plurality of locations in the blood vessel region corresponding to a plurality of phases of a cardiac cycle of the subject;

determine a plurality of flow rate values of blood flowing in the blood vessel corresponding to the plurality of cross-sectional area values, based on the image data set;

determine a hemodynamic model based on the plurality of cross-sectional area values and the plurality of blood flow rate values; and

determine an arterial pulse wave velocity based on the hemodynamic model, wherein the arterial pulse wave velocity is representative of a health condition of the blood vessel.

13. The system of claim 12, wherein the imaging modality is a magnetic resonance imaging modality.

14. The system of claim 12, wherein the processor module is configured to determine the hemodynamic model comprising a one dimensional pulse propagation model and a lumped resistance-capacitance-resistance (RCR) model coupled to the one dimensional pulse propagation model.

15. The system of claim 12, wherein processor module is configured to determine the hemodynamic model comprising a three dimensional computation fluid dynamic model and a lumped resistance-capacitance-resistance (RCR) model coupled to the three dimensional computation fluid dynamic model.

16. The system of claim 12, wherein processor module is configured to determine a mean blood pressure value based on a plurality of cuff measurements.

17. The system of claim 12, wherein processor module is configured to determine the hemodynamic model by determining at least one of a mean blood pressure value, a blood vessel compliance value, and a lumped model compliance value based on an optimization technique.

18. The system of claim 17, wherein the processor module is configured to perform the optimization technique based on a genetic algorithm.

19. The system of claim 17, wherein the processor module is configured to perform the optimization technique based on a gradient descent technique.

20. The system of claim 17, wherein the processor module is configured to perform the optimization technique using a cost function based on a difference between a plurality of predicted values obtained from the hemodynamic model and a plurality of corresponding measured values obtained from the imaging data set.

21. The system of claim 20, wherein the corresponding measured values are the cross-sectional area values of the blood vessel.

22. The system of claim 20, wherein the corresponding measure values are the blood flow rate values of the blood vessel.

23. A non-transitory computer readable medium having instructions to enable at least one processor module to:

receive an image data set comprising a plurality of images of a subject, from an imaging modality;

determine a blood vessel region in an image from the plurality of images, using an image segmentation technique;

determine a plurality of cross-sectional area values of a blood vessel at a plurality of locations in the blood vessel region corresponding to a plurality phases of a cardiac cycle of the subject;

determine a plurality of flow rate values of blood flowing in the blood vessel corresponding to the plurality of cross-sectional area values, based on the image data set;

determine a hemodynamic model based on the plurality of cross-sectional area values and the plurality of blood flow rate values; and

determine an arterial pulse wave velocity based on the hemodynamic model, wherein the arterial pulse wave velocity is representative of a health condition of the blood vessel.