METHOD FOR ESTIMATING HAEOMODYNAMIC PARAMETERS BY JOINT ESTIMATION OF THE PARAMETERS OF A GLOBAL PERFUSION MODEL

• US 20120141005A1
• Filed: 06/01/2010
• Published: 06/07/2012
• Est. Priority Date: 06/05/2009
• Status: Active Grant

• Current Assignee: Olea medical
• Original Assignee: Olea medical
• Inventors: Djeridane, Faycal, Pautot, Fabrice
• Granted Patent: US 8,792,701 B2
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/0263 using NMR
• CPC Class Codes: A61B 5/0275 using tracers, e.g. dye dilution
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/481 involving the use of contrast agents
• CPC Class Codes: A61B 6/501 for diagnosis of the head, e.g. neuroimaging or craniography
• CPC Class Codes: A61B 6/504 for diagnosis of blood vessels, e.g. by angiography
• CPC Class Codes: A61B 6/507 for determination of haemodynamic parameters, e.g. perfusion CT
• CPC Class Codes: G01R 33/5601 involving use of a contrast agent for contrast manipulation, e.g. a paramagnetic, super-paramagnetic, ferromagnetic or hyperpolarised contrast agent
• CPC Class Codes: G01R 33/5635 Angiography, e.g. contrast-enhanced angiography [CE-MRA] or time-of-flight angiography [TOF-MRA]
• CPC Class Codes: G01R 33/56366 Perfusion imaging
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

The invention relates to a method for estimating haemodynamic perfusion parameters of an elementary volume—termed a voxel—of an organ, from perfusion signals by jointly estimating the parameters of an optionally limited comprehensive perfusion model. The invention moreover relates to a processing unit of a perfusion imaging analysis system, adapted for carrying out such a method and for delivering the estimated parameters according to an appropriate format to a human-machine interface able to represent said estimated parameters for a user.

Claims

1. A method for estimating one or more haemodynamic perfusion parameters of an elementary volume—

referred to as a voxel—

of an organ, said method being implemented by a processing unit of a perfusion imaging analysis system, and comprising a step for estimating one or more haemodynamic parameters from perfusion signals S(t), wherein the step for estimating the haemodynamic parameter or parameters includes a joint estimation of the parameters Θ

of a global perfusion model comprising;

a first relationship between the perfusion signal S(t) and a concentration C(t) of a contrast agent circulating in said voxel over a time t;

a second relationship between the concentration C(t) of a contrast agent, the blood flow BF, a parametric or semiparametric model C_a(t,Θ

_a) of an arterial input function and a parametric or semiparametric model R(t,Θ

_R) of a complementary cumulative distribution function of the transit time in the voxel, Θ

_a and Θ

_R being respectively the parameters of the models C_a(t,Θ

_a) and R(t,Θ

_R).

2. A method according to claim 1, wherein:

the perfusion signal S(t) was previously obtained from digital images delivered by a magnetic resonance perfusion imaging apparatus;

the first relationship of the global perfusion model is expressed by S(t)=S₀e<sup>−

k·

TE·

C(t)</sup> where S₀ is the mean intensity of the signal before the arrival of the contrast agent in the voxel, TE is an echo time and k is a non-zero constant;

the second relationship of the global perfusion model is expressed by C(t)=η

·

BF·

C_a(t,Θ

_a)R(t,Θ

_R) where designates the convolution product, η

is a non-zero constant and BF is the blood flow in the voxel.

3. A method according to claim 1, wherein:

the perfusion signal S(t) was previously obtained from digital images delivered by a computed tomography perfusion imaging apparatus;

the first relationship of the global perfusion model is a proportionality equation S(t)=α

·

C(t) where a where is a non-zero constant;

the second relationship of the global perfusion model is expressed by C(t)=η

·

BF·

C_a(t,Θ

_a)R(t,Θ

_R) where designates the convolution product, 77 is a non-zero constant and BF is the blood flow in the voxel.

4. A method according to claim 1, comprising successive iterations for a plurality of voxels in question.

5. A method according to claim 1, where the joint estimation of the parameters Θ

of the global perfusion model used by the processing unit is implemented by means of the Bayes method or the method of Maximum Likelihood methods or the method non-linear least squares.

6. A method according to claim 1, further including a step for quantifying the goodness-of-fit of the global perfusion model to experimental perfusion data D=[S(t_i), . . . , S(t_N)] by a calculation of a probability

7. A method according to claim 1, further comprising a prior step for choosing a global perfusion model from a plurality of models.

8. A method according to claim 6, further comprising iteratively implementing the method by the processing unit for each global perfusion model of the plurality of models.

9. A method according to claim 8, wherein the estimated parameters according to the perfusion model, whose probability is the greatest, are delivered.

10. A method according to claim 1, wherein the model Ca(t,Θ

_a) of an arterial input function is a so-called “

tri-gamma”

model with twelve parameters defined by;

11. A method according to claim 1, wherein the model R(t,Θ

_R) of a complementary cumulative distribution function of the transit time in the voxel is a so-called “

corrected integral gamma”

model with two parameters defined by;

12. A method according to claim 1, wherein the global perfusion model comprises a third relationship Ψ

(Θ

a)=0 between the parameters Θ

a of the model of the arterial input function Ca(tΘ

_a).

13. A method according to claim 12, wherein the third relationship Ψ

(Θ

a)=0 of the global perfusion model is expressed as

14. A method according to claim 12, wherein the third relationship of the global perfusion model Ψ

(Θ

a)=0 is expressed as

15. A method according to claim 1, further comprising a step for calculating complementary information in the form of an estimated arterial input function C_a(t,).

16. A method according to claim 1, further comprising a step for calculating complementary information in the form of a estimated complementary cumulative distribution function R(t,).

17. A method according to claim 1, further comprising a step for calculating complementary information in the form of a confidence interval associated with a parameter of the global perfusion model.

18. A method according to claim 1, further comprising a step for calculating complementary information in the form of a bet on a confidence interval associated with a parameter of the global perfusion model.

19. A method according to claim 1, further comprising a step for delivering one or more estimated haemodynamic parameters to a human-machine interface configured to restore said estimated parameters to a user.

20. A method according to claim 15, further comprising a step for delivering any complementary information to a human-machine interface configured to restore to a user the estimated parameters and the complementary information.

21. A processing unit comprising storage means, means for communicating with the outside world and processing means, wherein:

the means for communicating are configured to receive from the outside world a signal S(t) obtained by perfusion imaging;

the storage means comprise a global perfusion model, said model comprising;

a first relationship between the perfusion signal S(t) and a concentration C(t) of a contrast agent circulating in a voxel over the course of time t;

a second relationship between the blood flow BF in the voxel, the concentration C(t), a parametric or semiparametic model C_a(t,Θ

_a) of an arterial input function and a parametric or semiparametric model R(t,Θ

_R) of a complementary cumulative distribution function of the transit time in the voxel, Θ

_a and Θ

_R being respectively the parameters of the models; and

the processing means are configured to implement a method for estimating haemodynamic perfusion parameters according to claim 1.

22. A processing unit comprising storage means, means for communicating with the outside world and processing means, wherein:

the means for communicating are configured to receive digital perfusion images from the outside world;

the storage means comprise a global perfusion model, said model comprising;

a first relationship between a perfusion signal S(t) and a concentration C(t) of a contrast agent circulating in a voxel in the course of time t;

a second relationship between the concentration C(t), the blood flow BF, a parametric or semiparametic model C_a(t,Θ

_a) of an arterial input function and a parametric or semiparametric model R(t,Θ

_R) of a complementary cumulative distribution function of the transit time in the voxel, Θ

_a and Θ

_R being respectively the parameters of the models; and

the processing means are adapted to determine a perfusion signal S(t) from perfusion images received and to implement a method for estimating one or several haemodynamic perfusion parameters according to claim 1.

23. A processing unit according to claim 21, wherein the storage means comprise a global perfusion model, said model comprising a third relationship Ψ

(Θ

_a)=0 between the parameters Θ

_a of the model of the arterial input function C_a(t,Θ

_a), and wherein the processing means are adapted to estimate one or more haemodynamic perfusion parameters.

24. A processing unit according to claim 21, wherein the means for communicating deliver one or more estimated parameters according to an appropriate format to a human-machine interface configured to restore said estimated parameters to a user.

25. A perfusion imaging analysis system comprising a processing unit according to claim 21 and a human-machine interface configured to restore to a user one or more parameters estimated by said processing unit.