System and method for adaptive bolus chasing computed tomography (CT) angiography

• US 20060178836A1
• Filed: 08/29/2005
• Published: 08/10/2006
• Est. Priority Date: 08/31/2004
• Status: Active Grant

• Current Assignee: University of Iowa Research Foundation
• Original Assignee: University of Iowa Research Foundation
• Inventors: Vannier, Michael W., Wang, Ge, Bai, Er-wei
• Granted Patent: US 7,522,744 B2
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: A61B 6/027 characterised by the use of a particular data acquisition trajectory, e.g. helical or spiral
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 7/0016 involving temporal comparison
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A method of utilizing bolus propagation and control for contrast enhancement comprises measuring with an imaging device a position of a bolus moving along a path in a biological structure. The method further comprises predicting a future position of the bolus using a simplified target model and comparing the predicted future position of the bolus with the measured position of the bolus. A control action is determined to eliminate a discrepancy, if any, between the predicted position of the bolus and the measured position of the bolus and the relative position of the imaging device and the biological structure is adaptively adjusted according to the control action to chase the motion of the bolus.

Claims

1. A method of utilizing bolus propagation and control for contrast enhancement comprising:

measuring with an imaging device a position of a bolus moving along a path in a biological structure;

predicting a future position of the bolus using a simplified target model;

comparing the predicted future position of the bolus with the measured position of the bolus;

determining a control action to eliminate a discrepancy, if any, between the predicted position of the bolus and the measured position of the bolus; and

adaptively adjusting the relative position of the imaging device and the biological structure according to the control action to chase the motion of the bolus.

2. The method of claim 1, wherein adaptively adjusting the relative position of the imaging device and the biological structure according to the control action to chase the motion of the bolus comprises adaptively transporting a table on which the biological structure is positioned according to the control action.

3. The method of claim 1, wherein adaptively adjusting the relative position of the imaging device and the biological structure according to the control action to chase the motion of the bolus comprises adaptively transporting a gantry surrounding a table on which the biological structure is positioned according to the control action.

4. The method of claim 1, wherein adaptively adjusting the relative position of the imaging device and the biological structure according to the control action to chase the motion of the bolus comprises adaptively transporting a table on which the biological structure is positioned and transporting a gantry surrounding the table according to the control action.

5. The method of claim 1, wherein the simplified target model determines the predicted position of the bolus based on current and past bolus positions and bolus velocities.

6. The method of claim 5, wherein the current and past bolus positions and velocities are provided by imaging reconstruction algorithms.

7. The method of claim 1, wherein the simplified target model is a non-parametric model.

8. The method of claim 7, wherein predicting the future position of the bolus comprises:

estimating the future position by {circumflex over (p)}_b((i+1)Δ

t)=p_b(IΔ

t)+{circumflex over (v)}(iΔ

t)Δ

t, wherein {circumflex over (v)}(iΔ

t) and {circumflex over (p)}_b(iΔ

t) denote an estimate of the bolus velocity and position v(iΔ

t) and p_b(iΔ

t) at time iΔ

t, respectively.

9. The method of claim 8, wherein, p_b(IΔ

t)=p_b((i−

1)Δ

t)+v((i−

1)Δ

t)Δ

t.

10. The method of claim 8, wherein {circumflex over (v)}(iΔ

t)={circumflex over (v)}((i−

1)Δ

t)+μ

[p_b(iΔ

t)−

p_b((i−

1)Δ

t)−

{circumflex over (v)}((i−

1)Δ

t)Δ

t].

11. The method of claim 8, wherein the imaging device or a portion thereof is adaptively transported to {circumflex over (p)}_b((i+1)Δ

t) at time (i+1)Δ

t by setting u(iΔ

t)={circumflex over (p)}_b(i+t+Δ

t) where p_b(iΔ

t+Δ

t)=u(iΔ

t) and u(iΔ

t) is the control action.

12. The method of claim 8, wherein the imaging device comprises a table and a gantry surrounding the table and wherein the table or gantry surrounding the table is adaptively transported to {circumflex over (p)}_b((i+1)Δ

t) at time (i+1)Δ

t by setting u(iΔ

t)={circumflex over (p)}_b(iΔ

t+Δ

t).

13. The method of claim 1, wherein the simplified target model is an extended Hammerstein model.

14. The method of claim 13, wherein the extended Hammerstein model is an approximate linear model with some parameters entering nonlinearly.

15. The method of claim 14, wherein predicting the future position of the bolus comprises:

estimating the parameters σ

, τ and

t_c wherein the movement of the bolus is described by

$b<br><br>\left(t,y\right)=c<br><br>\left(t\right)*\frac{1}{\mathrm{\sigma <br><br>}<br><br>\left(y\right)<br><br>\sqrt{2<br><br>\mathrm{\pi <br><br>}}}<br><br>{\mathrm{e<br><br>}}^{-\frac{{\left(t-t_c<br><br>\left(y\right)\right)}^2}{2<br><br>{\mathrm{\sigma <br><br>}}^2<br><br>\left(y\right)}}*\frac{1}{\mathrm{\tau <br><br>}<br><br>\left(y\right)}<br><br>{\mathrm{e<br><br>}}^{\frac{t}{\mathrm{\tau <br><br>}<br><br>\left(y\right)}}$

and wherein the function c(t) is determined by the injection of a bolus into a subject and the parameters t_c,τ

and σ

depend on a location y within the subject.

16. The method of claim 14, wherein the extended Hammerstein model comprises three linear systems b(t,y₁), b(t,y₂), b(t,y₃).

17. The method of claim 16, wherein y≠

y₁, y₂, y₃ b(t,y) is obtained by interpolation.

18. The method of claim 16, wherein y≠

y₁, y₂, y₃ b(t,y) is obtained by extrapolation.

19. The method of claim 14, further comprising inputing an individualized parameter into the extended Hammerstein model.

20. The method of claim 19, wherein the individualized parameter is selected from the group consisting of an ECG signal, heart rate, rhythm, stroke volume, contractility, cardiac preload and cardiac afterload.

21. The method of claim 20, wherein the ECG signal is input into the extended Hammerstein model to predict a surge of the bolus during a systole stage.

22. The method of claim 13, wherein the extended Hammerstein model is described by

${\stackrel{⋒}{p}}_{}$

b ⁡

( ⅈ

⁢

 

⁢

Δ

⁢

 

⁢

t + Δ

⁢

 

⁢

t ) = { p b ⁡

( ⅈ

⁢

 

⁢

Δ

⁢

 

⁢

t ) s ⁡

( k ) + q ⁡

( k ) ≤

i ≤

s ⁡

( k + 1 ) p b ⁡

( ⅈ

⁢

 

⁢

Δ

⁢

 

⁢

t ) + c s ⁡

( k ) = i <

s ⁡

( k ) + q ⁡

( k )

for some q(k) and c, where c captures the surge of the bolus during the systole stage and s(k) denotes the S phase of a kth ECG signal.

23. The method of claim 22, further comprising inputing an individualized parameter into the extended Hammerstein model.

24. The method of claim 23, wherein the individualized parameter is selected from the group consisting of an ECG signal, heart rate, rhythm, stroke volume, contractility, cardiac preload and cardiac afterload.

25. The method of claim 24, wherein the ECG signal is input into the extended Hammerstein model to predict a surge of the bolus during a systole stage.

26. The method of claim 13, wherein the extended Hammerstein model is a dynamic nonlinear system followed by a linear dynamic system.

27. The method of claim 26, further comprising inputing an individualized parameter into the extended Hammerstein model.

28. The method of claim 27, wherein the individualized parameter is selected from the group consisting of an ECG signal, heart rate, rhythm, stroke volume, contractility, cardiac preload and cardiac afterload.

29. The method of claim 28, wherein the ECG signal is input into the extended Hammerstein model to predict a surge of the bolus during systole.

30. The method of claim 1, wherein the simplified target model is a descriptive model.

31. The method of claim 30, wherein the descriptive model is a fuzzy model.

32. The method of claim 30, wherein the descriptive model is a rule based model.

33. The method of claim 1, wherein the imaging device comprises a table and a gantry surrounding the table and wherein the control action defines the amount, direction and velocity to adaptively transport the table or the gantry surrounding the table to chase the motion of the bolus.

34. The method of claim 1, wherein the control action is determined by a control law.

35. The method of claim 34, wherein the control law is selectable by a user.

36. The method of claim 34, wherein the control law is selected from the group consisting of PID controls, optimal controls, H_inf controls, robust controls, adaptive controls, expert controls, fuzzy controls, and intelligent controls.

37. The method of claim 1, wherein the imaging device comprises a table and a gantry surrounding the table and wherein the table or gantry surrounding the table is adaptively transported by an actuator that moves the table or gantry surrounding the table according to the determined control action.

38. The method of claim 1, wherein the imaging device comprises a table and a gantry surrounding the table and wherein the table and gantry surrounding the table are adaptively transported by an actuator.

39. The method of claim 1, further comprising performing digital subtraction angiography.

40. A system for utilizing bolus propagation and control for contrast enhancement comprising:

an imaging device for measuring a position of a bolus moving along a path in a biological structure;

a predictor comprising a processor programmed for predicting a future position of the bolus using a simplified target model;

a processor programmed for comparing the predicted future position of the bolus with the measured position of the bolus; and

a controller comprising a processor programmed for determining a control action to eliminate a discrepancy, if any, between the predicted position of the bolus and the measured position of the bolus and an actuator for adaptively adjusting the relative position of the imaging device and the biological structure according to the control action to chase the motion of the bolus.

41. The system of claim 40, wherein the biological structure is positioned on a table and the actuator adaptively adjusts the relative position of the imaging device and the biological structure according to the control action to chase the motion of the bolus by adaptively transporting the table according to the control action.

42. The system of claim 40, wherein the biological structure is positioned on a table and is surrounded by a gantry, and wherein the actuator adaptively adjusts the relative position of the imaging device and the biological structure according to the control action to chase the motion of the bolus by adaptively transporting the gantry according to the control action.

43. The system of claim 40, wherein the biological structure is positioned on a table and is surrounded by a gantry, and wherein the actuator adaptively adjusts the relative position of the imaging device and the biological structure according to the control action to chase the motion of the bolus by adaptively transporting the table and the gantry according to the control action.

44. The system of claim 40, wherein the predictor predicts the future position of the bolus using a simplified target model based on current and past bolus positions and bolus velocities.

45. The system of claim 44, wherein the current and past bolus positions and velocities are provided by imaging reconstruction algorithms.

46. The system of claim 40, further comprising an estimator, wherein the estimator estimates unknown parameters that are input into the simplified target model.

47. The system of claim 40, wherein the simplified target model is a non-parametric model.

48. The system of claim 47, wherein the predictor predicts the future position of the bolus by:

estimating the future position by {circumflex over (p)}_b((i+1)Δ

t)=p_b(iΔ

t)+{circumflex over (v)}(iΔ

t)Δ

t, wherein {circumflex over (v)}(iΔ

t) and {circumflex over (p)}_b(iΔ

t) denote an estimate of the bolus velocity and position v(iΔ

t) and p_b(iΔ

t) at time iΔ

t, respectively.

49. The system of claim 48, wherein, p_b(iΔ

t)=p_b((i−

1)Δ

t)+v((i−

1)Δ

t)Δ

t.

50. The system of claim 48, wherein {circumflex over (v)}(iΔ

t)={circumflex over (v)}((i−

1)Δ

t)+μ

[p_b(iΔ

t)−

p_b((i−

1)Δ

t)−

{circumflex over (v)}((i−

1)Δ

t)Δ

t].

51. The system of claim 48, wherein the actuator adaptively transports the imaging device or a portion thereof to {circumflex over (p)}_b((i+1)Δ

t) at time (i+1)Δ

t by setting

u(iΔ

t)={circumflex over (p)}_b(iΔ

t+Δ

t)

where p_b(iΔ

t+Δ

t)=u(iΔ

t) and u(iΔ

t) is the control action.

52. The system of claim 48, wherein the imaging device comprises a table and a gantry surrounding the table and the actuator adaptively transports the table or gantry to {circumflex over (p)}_b((i+1)Δ

t) at time (i+1)Δ

t by setting u(iΔ

t)={circumflex over (p)}_b(iΔ

t+Δ

t).

53. The system of claim 40, wherein the simplified target model is an extended Hammerstein model.

54. The system of claim 53, wherein the extended Hammerstein model is an approximate linear model with some parameters entering nonlinearly.

55. The system of claim 54, wherein the predictor predicts the future position of the bolus by:

estimating the parameters σ

, τ and

t_c wherein the movement of the bolus is described by

$b<br><br>\left(t,y\right)=c<br><br>\left(t\right)*\frac{1}{\mathrm{\sigma <br><br>}<br><br>\left(y\right)<br><br>\sqrt{2<br><br>\mathrm{\pi <br><br>}}}<br><br>{\mathrm{e<br><br>}}^{-\frac{{\left(t-t_c<br><br>\left(y\right)\right)}^2}{2<br><br>{\mathrm{\sigma <br><br>}}^2<br><br>\left(y\right)}}*\frac{1}{\mathrm{\tau <br><br>}<br><br>\left(y\right)}<br><br>{\mathrm{e<br><br>}}^{\frac{t}{\mathrm{\tau <br><br>}<br><br>\left(y\right)}}$

and wherein the function c(t) is determined by the injection of a bolus into a subject and the parameters t_c,τ

and σ

depend on a location y within the subject.

56. The system of claim 54, wherein the extended Hammerstein model comprises three linear systems b(t,y₁), b(t,y₂), b(t,y₃).

57. The system of claim 56, wherein y≠

y₁, y₂, y₃, b(t,y) is obtained by interpolation.

58. The system of claim 56, wherein y≠

y₁, y₂, y₃, b(t,y) is obtained by extrapolation.

59. The system of claim 53, wherein the predictor predicts the future position of the bolus by:

${\stackrel{⋒<br><br>}{p}}_b(\text{\hspace{1em}<br><br>}<br><br>\mathrm{i<br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>t<br><br>\text{\hspace{1em}<br><br>}+\text{\hspace{1em}<br><br>}<br><br>\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>t)=\{\begin{array}{cc}{p}_b<br><br>\left(\mathrm{i<br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>t\right)& s<br><br>\left(k\right)<br><br>\text{\hspace{1em}<br><br>}+\text{\hspace{1em}<br><br>}<br><br>q<br><br>\left(k\right)<br><br>\text{\hspace{1em}<br><br>}\le <br><br>\text{\hspace{1em}<br><br>}<br><br>i<br><br>\text{\hspace{1em}<br><br>}\le <br><br>\text{\hspace{1em}<br><br>}<br><br>s<br><br>\left(k<br><br>\text{\hspace{1em}<br><br>}+\text{\hspace{1em}<br><br>}<br><br>1\right)\\ p_b<br><br>\left(\mathrm{i<br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>t\right)<br><br>\text{\hspace{1em}<br><br>}+\text{\hspace{1em}<br><br>}<br><br>c& s<br><br>\left(k\right)<br><br>\text{\hspace{1em}<br><br>}=\text{\hspace{1em}<br><br>}<br><br>i<br><br>\text{\hspace{1em}<br><br>}<<br><br>\text{\hspace{1em}<br><br>}<br><br>s<br><br>\left(k\right)<br><br>\text{\hspace{1em}<br><br>}+\text{\hspace{1em}<br><br>}<br><br>q<br><br>\left(k\right)\end{array}$

for some q(k) and c, where c captures a surge of the bolus during a systole stage and s(k) denotes the S phase of a kth ECG signal.

60. The system of claim 59, further comprising inputing an individualized parameter into the extended Hammerstein model.

61. The system of claim 60, wherein the individualized parameter is selected from the group consisting of an ECG signal, heart rate, rhythm, stroke volume, contractility, cardiac preload and cardiac afterload.

62. The system of claim 61, wherein the ECG signal is input into the extended Hammerstein model to predict a surge of the bolus during systole.

63. The system of claim 40, wherein the simplified target model is a descriptive model.

64. The system of claim 63, wherein the descriptive model is a fuzzy model.

65. The system of claim 63, wherein the descriptive model is a rule based model.

66. The system of claim 40, wherein the imaging device comprises a table and a gantry surrounding the table and wherein the controller determines a control action that defines the amount, direction and velocity for the actuator to adaptively transport the table or the gantry surrounding the table to chase the motion of the bolus.

67. The system of claim 40, wherein the control action is determined by a control law.

68. The system of claim 40, wherein the control law is selectable by a user.

69. The system of claim 68, wherein the control law is selected from the group consisting of PID controls, optimal controls, H_inf controls, robust controls, adaptive controls, expert controls, fuzzy controls, and intelligent controls.

70. The system of claim 40, wherein the imaging device comprises a table and a gantry surrounding the table and wherein the table or gantry surrounding the table is adaptively transported by the actuator which moves the table or gantry surrounding the table according to the determined control action.

71. The system of claim 40, wherein the imaging device comprises a table and a gantry surrounding the table and wherein the table and gantry surrounding the table are adaptively transported by the actuator.

72. A computer readable medium having computer readable program code for utilizing bolus propagation and control for contrast enhancement comprising:

program code for measuring with an imaging device a position of a bolus moving along a path in a biological structure;

program code for predicting a future position of the bolus using a simplified target model;

program code for comparing the predicted future position of the bolus with the measured position of the bolus;

program code for determining a control action to eliminate a discrepancy, if any, between the predicted position of the bolus and the measured position of the bolus; and

program code for adaptively adjusting the relative position of the imaging device and the biological structure according to the control action to chase the motion of the bolus.