System and method for adaptive bolus chasing computed tomography (CT) angiography Number:7,522,744 from the United States Patent and Trademark Office (PTO) owispatent 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.<H angiography, tomography, System, and, meth, 7522744.html, Patent, pto, 7522744

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

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.

Patent Number: 7,522,744 Issued on 04/21/2009 to Bai, et al.

Inventors: Bai; Er-wei (Iowa City, IA), Wang; Ge (Iowa City, IA), Vannier; Michael W. (Iowa City, IA)
Assignee: University of Iowa Research Foundation (Iowa City, IA)

Appl. No.: 11/215,733

Filed: August 29, 2005

Related U.S. Patent Documents


Application Number	Filing Date	Patent Number	Issue Date
60605865	Aug., 2004

Current U.S. Class: 382/100 ; 378/4; 702/19

Current International Class: G06K 9/00 (20060101); A61B 6/00 (20060101); G06F 19/00 (20060101)

Field of Search: 382/100,128-134 600/407,410,411,415-420,425,431,436 378/4,8,20-28,901 702/19,150,182,183 128/920 424/9.4

References Cited [Referenced By]

U.S. Patent Documents


5713358	February 1998	Mistretta
5830143	November 1998	Mistretta
5839440	November 1998	Liou et al.
5960054	September 1999	Freeman et al.
6075836	June 2000	Ning
6230040	May 2001	Wang et al.
6351513	February 2002	Bani-Hashemi et al.
6389104	May 2002	Bani-Hashemi et al.
6397098	May 2002	Uber et al.
6535821	March 2003	Wang et al.
6577887	June 2003	Wolff et al.
6597938	July 2003	Liu
6614878	September 2003	Bogatu et al.
2002/0010551	January 2002	Wang et al.
2003/0036694	February 2003	Liu

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Primary Examiner: Tabatabai; Abolfazl
Attorney, Agent or Firm: Ballard Spahr Andrews & Ingersoll, LLP

Government Interests

ACKNOWLEDGMENT

This invention was made partially with government support under NIH/NIBIB grants EB001685 and EB002667-01 awarded by the National Institutes of Health. The government has certain rights in the invention.

Parent Case Text

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 60/605,865, filed Aug. 31, 2004. The aforementioned application is herein incorporated in its entirety by reference.

Claims

What is claimed is:

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, wherein the simplified target model is a non-parametric model and wherein predicting the future position of the bolus comprises estimating the future position by {circumflex over (p)}.sub.b((i+1).DELTA.t)=p.sub.b(i.DELTA.t)+{circumflex over (v)}(i.DELTA.t).DELTA.t, wherein {circumflex over (v)}(i.DELTA.t) and {circumflex over (p)}.sub.b(i.DELTA.t)denote an estimate of the bolus velocity and position v(i.DELTA.t) and p.sub.b(i.DELTA.t) at time i.DELTA.t, respectively; 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, p.sub.b(i.DELTA.t)=p.sub.b((i-1).DELTA.t)+v((i-1).DELTA.t).DELTA.t.

8. The method of claim 1, wherein {circumflex over (v)}(i.DELTA.t)={circumflex over (v)}((i-1).DELTA.t)+.mu.[p.sub.b(i.DELTA.t)-p.sub.b((i-1).DELTA.t)-{circu- mflex over (v)}((i-1).DELTA.t).DELTA.t].

9. The method of claim 1, wherein the imaging device or a portion thereof is adaptively transported to {circumflex over (p)}.sub.b((i+1).DELTA.t) at time (i+1).DELTA.t by setting u(i.DELTA.t)={circumflex over (p)}.sub.b(i.DELTA.t+.DELTA.t) where p.sub.b(i.DELTA.t+.DELTA.t)=u(i.DELTA.t) and u(i.DELTA.t) is the control action.

10. 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 to {circumflex over (p)}.sub.b((i+1).DELTA.t) at time (i+1).DELTA.t by setting u(i.DELTA.t)={circumflex over (p)}.sub.b(i.DELTA.t+.DELTA.t).

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

12. The method of claim 11, wherein the extended Hammerstein model is an approximate linear model with some parameters entering nonlinearly.

13. The method of claim 12, wherein predicting the future position of the bolus comprises: estimating the parameters .sigma., .tau. and t.sub.c wherein the movement of the bolus is described by .function..function..sigma..function..times..times..pi..times.e.function.- .times..sigma..function..tau..function..times.e.tau..function. ##EQU00006## and wherein the function c(t) is determined by the injection of a bolus into a subject and the parameters t.sub.c, .tau. and .sigma. depend on a location y within the subject.

14. The method of claim 12, wherein the extended Hammerstein model comprises three linear systems b(t, y.sub.1), b(t, y.sub.2), b(t, y.sub.3).

15. The method of claim 14, wherein y.+-.y.sub.1, y.sub.2, y.sub.3, b(t, y) is obtained by interpolation.

16. The method of claim 14, wherein y.+-.y.sub.1, y.sub.2, y.sub.3, b(t, y) is obtained by extrapolation.

17. The method of claim 12, further comprising inputting an individualized parameter into the extended Hammerstein model.

18. The method of claim 17, 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.

19. The method of claim 18, wherein the ECG signal is input into the extended Hammerstein model to predict a surge of the bolus during a systole stage.

20. The method of claim 11, wherein the extended Hammerstein model is described by .function.I.times..times..DELTA..times..times..DELTA..times..times..funct- ion.I.times..times..DELTA..times..times..function..function..ltoreq..ltore- q..function..function.I.times..times..DELTA..times..times..function.<.f- unction..function. ##EQU00007## 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.

21. The method of claim 20, further comprising inputting an individualized parameter into the extended Hammerstein model.

22. The method of claim 21, 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.

23. The method of claim 22, wherein the ECG signal is input into the extended Hammerstein model to predict a surge of the bolus during a systole stage.

24. The method of claim 11, wherein the extended Hammerstein model is a dynamic nonlinear system followed by a linear dynamic system.

25. The method of claim 24, further comprising inputting an individualized parameter into the extended Hammerstein model.

26. The method of claim 25, 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.

27. The method of claim 26, wherein the ECG signal is input into the extended Hammerstein model to predict a surge of the bolus during systole.

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

29. The method of claim 28, wherein the descriptive model is a fuzzy model.

30. The method of claim 28, wherein the descriptive model is a rule based model.

31. 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.

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

33. The method of claim 32, wherein the control law is selectable by a user.

34. The method of claim 32, 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.

35. 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.

36. 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.

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

38. 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, wherein the simplified target model is a non-parametric model and wherein the predictor predicts the future position of the bolus by estimating the future position by {circumflex over (p)}.sub.b((i+1).DELTA.t)=p.sub.b(i.DELTA.t)+{circumflex over (v)}(i.DELTA.t).DELTA.t, wherein {circumflex over (v)}(i.DELTA.t) and {circumflex over (p)}.sub.b(i.DELTA.t) denote an estimate of the bolus velocity and position v(i.DELTA.t) and p.sub.b(i.DELTA.t) at time i.DELTA.t, respectively; 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.

39. The system of claim 38, 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.

40. The system of claim 38, 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.

41. The system of claim 38, 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.

42. The system of claim 38, 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.

43. The system of claim 42, wherein the current and past bolus positions and velocities are provided by imaging reconstruction algorithms.

44. The system of claim 38, further comprising an estimator, wherein the estimator estimates unknown parameters that are input into the simplified target model.

45. The system of claim 38, wherein, p.sub.b(i.DELTA.t)=p.sub.b((i-1).DELTA.t)+v((i i-1).DELTA.t).DELTA.t.

46. The system of claim 38, wherein {circumflex over (v)}(i.DELTA.t)={circumflex over (v)}((i-1).DELTA.t)+.mu.[p.sub.b(i.DELTA.t)-p.sub.b((i-1).DELTA.t)-{circu- mflex over (v)}((i-1).DELTA.t).DELTA.t].

47. The system of claim 38, wherein the actuator adaptively transports the imaging device or a portion thereof to {circumflex over (p)}.sub.b((i+1).DELTA.t) at time (i+1).DELTA.t by setting u(i.DELTA.t)={circumflex over (p)}.sub.b(i.DELTA.t+.DELTA.t) where p.sub.b(i.DELTA.t+.DELTA.t) and u(i.DELTA.t)is the control action.

48. The system of claim 38, 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)}.sub.b((i+1).DELTA.t) at time (i+1).DELTA.t by setting u(i.DELTA.t)={circumflex over (p)}.sub.b(i.DELTA.t+.DELTA.t).

49. The system of claim 38, wherein the simplified target model is an extended Hammerstein model.

50. The system of claim 49, wherein the extended Hammerstein model is an approximate linear model with some parameters entering nonlinearly.

51. The system of claim 50, wherein the predictor predicts the future position of the bolus by: estimating the parameters .sigma., .tau. and t.sub.c wherein the movement of the bolus is described by .times..times..sigma..times..times..times..pi..times.e.times..times..sigm- a..times..tau..times..times.e.tau..times. ##EQU00008## and wherein the function c(t) is determined by the injection of a bolus into a subject and the parameters t.sub.c, .tau. and .sigma. depend on a location y within the subject.

52. The system of claim 50, wherein the extended Hammerstein model comprises three linear systems b(t, y.sub.1), b(t, y.sub.2), b(t, y.sub.3).

53. The system of claim 52, wherein y.+-.y.sub.1, y.sub.2, y.sub.3, b(t, y) is obtained by interpolation.

54. The system of claim 52, wherein y.+-.y.sub.1, y.sub.2, y.sub.3, b(t, y) is obtained by extrapolation.

55. The system of claim 49, wherein the predictor predicts the future position of the bolus by: .times.I.times..times..DELTA..times..times..times..times..DELTA..times..t- imes..function.I.times..times..DELTA..times..times..function..times..times- ..function..times..ltoreq..times..times..ltoreq..times..function..times..t- imes..function.I.times..times..DELTA..times..times..times..times..function- ..times..times..times.<.times..function..times..times..function. ##EQU00009## 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.

56. The system of claim 55, further comprising inputting an individualized parameter into the extended Hammerstein model.

57. The system of claim 56, 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.

58. The system of claim 57, wherein the ECG signal is input into the extended Hammerstein model to predict a surge of the bolus during systole.

59. The system of claim 38, wherein the simplified target model is a descriptive model.

60. The system of claim 59, wherein the descriptive model is a fuzzy model.

61. The system of claim 59, wherein the descriptive model is a rule based model.

62. The system of claim 38, 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.

63. The system of claim 38, wherein the control action is determined by a control law.

64. The system of claim 38, wherein the control law is selectable by a user.

65. The system of claim 64, 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.

66. The system of claim 38, 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.

67. The system of claim 38, 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.

68. 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, wherein the simplified target model is a non-parametric model and wherein predicting the future position of the bolus comprises estimating the future position by {circumflex over (p)}.sub.b((i+1).DELTA.t)=p.sub.b(i.DELTA.t)+{circumflex over (v)}(i.DELTA.t).DELTA.t, wherein {circumflex over (v)}(i.DELTA.t) and {circumflex over (p)}.sub.b(i.DELTA.t) denote an estimate of the bolus velocity and position v(i.DELTA.t) and p.sub.b(i.DELTA.t) at time i.DELTA.t, respectively; 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.

Description

BACKGROUND OF THE INVENTION

1. Field of Invention

The present invention generally relates to bolus-chasing angiography wherein imaging data is analyzed more effectively, tolerating greater modeling errors and uncertainties, using more powerful and robust control techniques and incorporating faster and more robust identification/estimation algorithms and techniques.

2. Description of Prior Art

As known in the prior art, administration of a contrast material or bolus provides a short temporal window for optimally imaging the vasculature, lesions and tumors. Optimization of contrast enhancement becomes increasingly crucial with the wide use of CT and Magnetic Resonant Image ("MRI") technology, given the dramatically shortened image acquisition time. Recently, CT began a transition into sub-second scanning, cone-beam geometry and real-time imaging with the introduction of multi-slice/cone-beam systems.

A number of clinical studies were reported on contrast enhancement for CT in the past. However, the existing results on modeling of CT contrast bolus dynamics are very limited.

To obtain the highest image quality in CT angiography at the lowest dosage of contrast material, strategies for bolus administration and CT data acquisition must be individualized and optimized. It is desirable to scan when the intravascular concentration of contrast material is at its peak.

Scanning too early may result in over-estimation of stenosis, while scanning too late may result in overlap of venous structures.

Three methods have been developed to individualize scan timing: (1) test-bolus timing, (2) region of interest (ROI) threshold triggering, and (3) visual cue triggering.

For the test-bolus method, there is a risk of decreasing target lesion conspicuity due to equilibration of the test-bolus. For the two triggering methods, they are vulnerable to patient motion, usually related to breathing, which may displace the target organ or vessel from the scan plane.

Moreover, one of the fundamental limitations with all the three methods is that they provide little data for matching the table/gantry translation to the contrast bolus propagation. Bolus dynamics is complicated by multiple interacting factors involving the contrast administration protocol, imaging techniques, and patient characteristics. In particular, the current patient table is translated at a pre-specified constant speed during data acquisition, which cannot be altered adaptively to chase the contrast bolus for optimally enhanced CT images.

With a pre-set scanning speed, it is difficult and often impossible to synchronize the central scanning plane with the longitudinal bolus position. This misalignment becomes more problematic to image quality when spiral scanning speed is fast (with multi-slice spiral CT), contrast volume is small and/or injection rate is high (leading to reduced peak duration), as well as when there are large or small capacity vessels, either from aneurysm formation or occlusive disease.

U.S. Pat. No. 6,535,821 (Wang et al.) (the '821 patent), which is incorporated herein in its entirety by reference thereto, discloses a system and method for optimization of contrast enhancement utilizing a bolus propagation model, a computerized predictor of the bolus position, and a real-time tomographic imaging system with an adaptive mechanism to move a patient and/or the imaging components (either the entire gantry or its essential parts).

In the '821 patent, a monitoring means for measuring the position of a bolus moving along a path in a biological structure is provided. A predicted position of the bolus is determined using a bolus propagation model with a set of parameters. The predicted position of the bolus is compared with the measured position of the bolus. A filtering means is provided for reconciling a discrepancy, if any, between the predicted position of the bolus and the measured position of the bolus, to derive a set of control parameters. Finally, a control means is provided for receiving the set of control parameters, to adaptively transport the table to chase the moving bolus.

While the system and method disclosed in the '821 patent represents a major improvement over prior systems and methods, there is a need for making the implementation of bolus chasing imaging in practice efficient and possible by reconstructing and analyzing imaging data in more effective ways, tolerating greater modeling errors and uncertainties, using more powerful and robust control techniques, and incorporating faster and more robust identification/estimation algorithms and techniques.

SUMMARY OF THE INVENTION

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.

A system for utilizing bolus propagation and control for contrast enhancement comprises an imaging device for measuring a position of a bolus moving along a path in a biological structure. The system further comprises a predictor comprising a processor programmed for predicting a future position of the bolus using a simplified target model and a processor programmed for comparing the predicted future position of the bolus with the measured position of the bolus. A controller comprising a processor is 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. The system further comprises 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.

Additional advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the specification. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BREIF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate one embodiment of the invention and together with the description, serve to explain the principles of the invention.

FIG. 1 is a block diagram illustrating an exemplary method of the present invention.

FIG. 2 is a schematic diagram illustrating an exemplary method of utilizing bolus propagation for adaptive CT angiography according to one aspect of the present invention.

FIG. 3 is a system diagram depicting an exemplary adaptive CT angiography system for performing the method depicted in FIGS. 1 and 2 according to one embodiment of the present invention.

FIG. 4 is a schematic diagram depicting an exemplary adaptive CT angiography system using a non-parametric model according to one embodiment of the present invention.

FIG. 5 shows computer simulation data of bolus-chasing CT angiography, where the actual bolus velocity in the various positions in the vascular tree is represented by the solid line and the controlled table velocity based on the adaptive control technique is represented by the dashed line. The actual velocity curve is modeled as a stepping function according to vascular tree level under physiologic conditions, taking into account changes between systolic and diastolic phases.

FIG. 6 shows plots of the simulated actual bolus position (solid line) and the controlled/predicted patient table position (dashed line). The maximum error is less than 1 mm.

FIG. 7 shows the simulated actual bolus position (solid line) and the controlled table position (dash-dotted line).

FIG. 8 shows the tracking error (mm) which is the difference between the imaging aperture and the simulated actual bolus peak position.

FIG. 9 shows an example of the actual bolus positon obtained clinically (dash-dotted line) and the controlled table postion (solid line).

FIG. 10 shows another example of the actual bolus positon obtained clinically (dash-dotted line) and the controlled table postion (solid line).

DETAILED DESCRIPTION

The present invention is more particularly described in the following description and examples that are intended as illustrative only since numerous modifications and variations therein will be apparent to those skilled in the art. As used in the specification and in the claims, "a," "an," and "the" can mean one or more, depending upon the context in which it is used. The system and method are now described with reference to the Figures, in which like numbers indicate like parts throughout the Figures.

Ranges may be expressed herein as from "about" one particular value, and/or to "about" another particular value. When such a range is expressed, another embodiment includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent "about," it will be understood that the particular value forms another embodiment. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint the term.

"Optional" or "optionally" means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where said event or circumstance occurs and instances where it does not.

By a "subject" or "patient" is meant an individual. The term subject or patient includes small or laboratory animals as well as primates, including humans. The term does not denote a particular age or sex. As used herein, "biological structure" includes a human or animal subject, or anatomical portions thereof.

The present invention can be utilized to provide adaptive bolus chasing CT angiography. The bolus-chasing problem is akin to that of tracking an airplane by radar. The difference is that bolus dynamics are much more complicated than the movement of an airplane. To follow an airplane, the next position of the airplane has to be estimated and predicted based on the current and previous positions of the airplane. In addition to prediction, the present invention consists of a control phase, i.e., once the next bolus position is predicted, the relative position of an imaging device and a biological structure being imaged can be adaptively adjusted to chase the motion of a bolus in the biological structure. For example, the imaging device can comprise a patient table and gantry, as is typical to current CT systems, and the patient table and/or gantry can be moved accordingly so that the bolus position and the imaging aperture of the imaging device are synchronized.

Bolus propagation is governed by a set of partial differential equations which contains a large number of patient and circulatory stage-dependent parameters. The full model of bolus propagation is fairly accurate provided that all parameters are available. Because these parameters are unknown, however, the full model has a little use in practice for adaptive bolus chasing CT angiography. Moreover, partial differential equations are not easy to use in practice and online estimation of such a large number of parameters in a very short time (typically CT angiography lasts about 20-30 seconds) is impossible.

Another commonly used model to describe bolus dynamics is the compartmental model that is also of little use for adaptive bolus chasing CT angiography. Again, the model is a set of equations involving a large number of unknown parameters that are patient and circulatory stage dependent. Obviously, parameters about the patient conditions, e.g., vessel radius at each stage of the vascular tree, are difficult to have in advance. Also, disease-state related parameters are impossible to be quantified prior to an angiogram. Further, the compartmental model describes contrast enhancement specific to a compartment (organ or vessel) instead of predicting the bolus dynamic as a function of time.

Since these models of the bolus dynamics are impractical, the system and method of chasing a bolus in a patient described herein is based on a simplified target model. One exemplary simplified target model that can be used is a simplified nonlinear model. For example, an extended Hammerstein model, which incorporates some of a patient's physiological parameters obtained on-line or off-line, can be used. A second exemplary simplified target model that can be used is a non-parametric model. A third exemplary simplified target model is a descriptive model.

These models predict bolus location based on available current and past bolus information, which can be obtained by real time imaging using a tomographic or other imaging system before the bolus arrives at its next (future) position. Using the predicted bolus location a controller can be used to adaptively adjust the relative position of the imaging device and the biological structure to chase the motion of the bolus. For example, the table and/or the gantry of an imaging device can be translated accordingly to capture image data. In this way, the bolus is chased by the imaging aperture to capture an optimal image. The simplified but target model (simplified target model) captures not every detail of the bolus dynamics but the key components including the bolus peak position and velocity.

By the adaptive control of the table/gantry motion, a variable pitch multi-slice/cone-beam scanning mode can be implemented. To perform the multi-slice/cone-beam CT angiography optimally, the pre-source collimator may be dynamically controlled longitudinally and/or transversely. This variable collimation mechanism reduces the radiation dose to the patient significantly.

The disclosed system and method include a bolus propagation model, a controller and an estimator/predictor. The model approximately reflects the bolus motion. The estimator/predictor estimates some parameters used in the model and computes the future position based on the model. The controller minimizes the discrepancy between the bolus peak position and the imaging aperture by synchronizing the bolus motion and the table/gantry translation.

The disclosed methods can be performed by an adaptive CT angiography system that can include hardware and software. The system and method are directed towards optimization of contrast enhancement utilizing (1) a simplified target model, for example, an extended Hammerstein model, or (2) a non-parametric model, which can be applied to CT angiography (CTA) that relies on bolus peak prediction, real-time CT observation and adaptive table/gantry transport or (3) a descriptive model which can be used with expert, fuzzy and intelligent controls.

The system and method can be applied to digital subtraction angiography (DSA), as well as other applications. For example, the bolus propagation modeling and imaging techniques of the present invention may be applied to arterial phase imaging of the liver and pancreas, and venous imaging of vital organs. These techniques may also be used for functional studies, such as cardiac motion and organ perfusion analysis.

A discrepancy, if any, can be reconciled between the predicted peak position of the bolus and the image data of the bolus to determine control actions. Because of a simplified target model is used, advanced control schemes, including robust, adaptive, optimal, expert, fuzzy and intelligent controllers, can be implemented.

Given a poorly known approximate individualized model of contrast bolus dynamics, the contrast wave peak may be locked in with a moving plane, slab and volume to depict anatomical/physiological/pathological features, based on interactions among real-time imaging based analysis, on-line estimation and robust control. In other words, the present invention targets a most important dynamic process in projective/tomographic imaging-contrast enhancement.

FIG. 1 is a block diagram illustrating an exemplary method 10 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 20. A future position of the bolus is predicted using a simplified target model 30 and compared with the measured position of the bolus 40. A control action is determined 60 to eliminate a discrepancy, if any, between the predicted position of the bolus and the measured position of the bolus 50. The relative position of the imaging device and the biological structure are adaptively adjusted according to the control action 70 to chase the motion of the bolus. At block 80, it is determined whether to end the imaging procedure. Such a determination can be made after adaptive adjustment at block 70, or if no discrepancy was identified between the measured and predicted position, at block 50. If it is determined that the imaging procedure is to end, then reconstruction and angiography can be preformed in block 90. If it is determined that the imaging procedure is not to end, then the position of the bolus can be measured within the biological structure at block 20.

The disclosed method can be practiced on a subject or a biological structure positioned on a table. For example, the biological structure may be placed on the table of an imaging device such as a CT scanner. The table of such a CT scanner is typically surrounded by a gantry which houses an imaging aperture. When the biological structure or subject is positioned on a table, the relative position of the imaging device and the biological structure can be adaptively adjusted according to the control action to chase the motion of the bolus by adaptively transporting the table on which the biological structure is positioned according to the control action. The relative position of the imaging device and the biological structure can also be adaptively adjusted according to the control action to chase the motion of the bolus by adaptively transporting the gantry surrounding a table on which the biological structure is positioned according to the control action. Moreover, both the gantry and the table can be adaptively transported to adaptively adjusting the relative position of the imaging device and the biological structure according to the control action.

In one aspect, the system and method relates to a computer-readable, digital storage device storing executable instructions which cause a processor to utilize bolus propagation for CT angiography in a biological structure for measuring with an imaging device a position of a bolus moving along a path in a biological structure and for predicting a future position of the bolus using a simplified target model. A simplified target model includes but is not limited to determining a (1) simplified nonlinear model, for example, an extended Hammerstein model, (2) a non-parametric model with a set of parameters prior to the arrival of the bolus at a location of the path (the parameters may be identified/estimated on-line), or (3) a descriptive model such as a fuzzy or rule based model. The predicted position of the bolus is compared with the image data associated with or indicating the measured position of the bolus and 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. Thus, the predicted and measured position of the bolus from the image data can be used to extrapolate a set of control parameters or a control action to eliminate the discrepancy. The relative position of the imaging device and the biological structure can be adaptively adjusted according to the control action to chase the motion of the bolus. Also provided herein is a computer readable medium having computer readable program code for utilizing bolus propagation and control for contrast enhancement. Such a computer readable medium comprises program code for measuring with an imaging device a position of a bolus moving along a path in a biological structure, for predicting a future position of the bolus using a simplified target model, for comparing the predicted future position of the bolus with the measured position of the bolus, 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 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.

The methods described above can further comprise performing digital subtraction angiography. In a further aspect of the system and method, real-time estimation/prediction and measurement of bolus position is much more precise and comprehensive than relatively straightforward estimation and observation of bolus propagation in current 2D X-ray and MR DSA. In the present invention, bolus chasing may be based on bolus propagation prediction and monitoring, and may be performed continuously and optimally. In other words, the current limitations of bolus chasing imaging of prior art technology (which is "no automatic feedback loop") may be drastically improved by coupling advanced control and imaging methods with state of the art apparati.

Further provided herein is 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. The system further comprises 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. The system also comprises 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. The disclosed system can be used for performing the methods disclosed herein. Moreover, as would be clear to one skilled in the art, although the system is described having components including a predictor, processor for comparison, and controller, each comprising a processor, that a one or more processors could be used to perform these functions of the system. Thus, one or any combination of processors can be used in the system for the predictor, comparison processor, and controller.

Simplified Target Models

For the disclosed system and methods, an accurate estimation and prediction of the future bolus position based on the current and past bolus information can be determined. Modeling is an important step in estimating and predicting the bolus position. It has been shown that the full model of bolus propagation is governed by a set of very large number of partial differential equations that are however too complex and impractical. To overcome the difficulties of the full model, a simplified target model is used.

A simplified target model can determine the predicted position of the bolus based on current and past bolus positions and velocities. The current and past bolus positions and velocities can be provided by imaging reconstruction algorithms. Imaging algorithms are know to those skilled in the art and can be based on image domain and/or projection domain analyses. The image domain approach uses images reconstructed using an appropriate CT algorithm, such as approximate or exact spiral/helical CT algorithms, for example approximate circular cone-beam algorithms. The projection domain approach may also effectively identify the contrast change rate, for example, via conversion from cone-beam data in to Radon data before checking for the difference in the Radon space.

As described