Systems and methodologies for proton computed tomography

• US 10,180,505 B2
• Filed: 07/15/2015
• Issued: 01/15/2019
• Est. Priority Date: 02/12/2010
• Status: Active Grant

• Current Assignee: LOMA Linda University Medical Center (Mission Healthcare Services LLC)
• Original Assignee: LOMA Linda University Medical Center (Mission Healthcare Services LLC), University of Haifa, University Of Wollongong
• Inventors: Censor, Yair, Penfold, Scott N., Schulte, Reinhard W.
• Primary Examiner(s): Negin, Russell S
• Application Number:
  US14/799,910
• Publication Number:
  US 20160077223A1
• Time in Patent Office: 1,280 Days
• Field of Search: None
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/4241 using energy resolving detectors, e.g. photon counting
• CPC Class Codes: A61B 6/4258 for detecting non x-ray radiation, e.g. gamma radiation A61B6/037 takes precedence
• CPC Class Codes: A61B 6/4266 characterised by using a plurality of detector units A61B6/4014 takes precedence
• CPC Class Codes: A61B 6/5205 involving processing of raw data to produce diagnostic data
• CPC Class Codes: A61B 6/583 using calibration phantoms
• CPC Class Codes: A61N 2005/1087 Ions; Protons
• CPC Class Codes: A61N 5/10 X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy A61N5/01 takes precedence
• CPC Class Codes: G01N 2223/419 computed tomograph
• CPC Class Codes: G01N 23/046 using tomography, e.g. computed tomography [CT]
• CPC Class Codes: G01T 1/17 Circuit arrangements not adapted to a particular type of detector pulse-selection circuits H03K, G01R
• CPC Class Codes: G01T 1/2985 In depth localisation, e.g. using positron emitters; Tomographic imaging (longitudinal and transverse section imaging; apparatus for radiation diagnosis sequentially in different planes, steroscopic radiation diagnosis); using external radiation sources A61B6/02
• CPC Class Codes: G06T 11/006 Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks

Abstract

Disclosed are systems, devices and methodologies relating to proton computed tomography. In some implementations, detection of protons can yield track information before and after an object for each proton so as to allow determination of a likely path of each proton within the object. Further, measurement of energy loss experienced by each proton allows determination that a given likely path results in a given energy loss. A collection of such data allows characterization of the object. In the context of energy loss, such a characterization can include an image map of relative stopping power of the object. Various reconstruction methodologies for obtaining such an image, including but not limited to superiorization of a merit function such as total variation, are disclosed. In some implementations, various forms of total variation superiorization methodology can yield excellent results while being computationally efficient and with reduced computing time. In some implementations, such a methodology can result in high quality proton CT images using relatively low dose of protons.

Claims

1. A method for performing computed tomography, the method comprising:

delivering a particle beam to an object, the particle beam comprising a plurality of positive ions;

measuring, with one or more particle detectors, a trajectory and an interaction quantity for individual positive ions in the particle beam that pass through the object;

generating a system of linear equations representing the passage of the individual positive ions through the object, the system of linear equations arranged in the form of Ax=b, where x is a discrete object vector representing an object parameter distribution, b is a vector that represents the measured interaction quantities of the individual positive ions resulting from interactions along their respective paths in the object, and A is a matrix comprising individual elements that correspond to an estimated intersection length of an individual positive ion in a corresponding voxel based at least in part on an estimated path through the object, wherein the system of linear equations has a plurality of solutions forming a set of feasible solutions;

determining, using one or more computer processors, a solution for the system of linear equations using a superiorization procedure comprising performing N superiorization cycles, each superiorization cycle comprising;

perturbing the discrete object vector x using a perturbation corresponding to a gradient of a merit function;

projecting the perturbed discrete object vector x using a perturbation resilient projection algorithm; and

replacing the discrete object vector x with the projected, perturbed discrete object vector x; and

generating a computed tomography image of the object based at least in part on the determined solution that comprises the discrete object vector x after the superiorization procedure has been performed,

wherein the number of cycles, N, is greater than or equal to 2.

2. The method of claim 1, wherein the determined solution has a superior characteristic with respect to the merit function than a solution determined without performing the step of perturbing the discrete object vector x.

3. The method of claim 1, wherein the method does not calculate proximity to the set of feasible solutions for the projected, perturbed discrete object vector x.

4. The method of claim 3, wherein the method does not calculate proximity to the set of feasible solutions for the discrete object vector x.

5. The method of claim 1 further comprising forming M blocks from the measured data.

6. The method of claim 1, wherein the perturbation resilient projection algorithm comprises a block-iterative projection algorithm.

7. The method of claim 1, wherein the interaction quantity comprises an energy loss of the positive ion resulting from its passage through the object.

8. The method of claim 1, wherein the object parameter distribution comprises a distribution of an electron density-based quantity.

9. The method of claim 8, wherein the electron density-based quantity comprises relative proton stopping power with respect to water.

10. The method of claim 1 further comprising calculating an estimate of an initial solution using a filtered backprojection reconstruction, the estimated initial solution being used as the discrete object vector x in the first cycle of the superiorization procedure.

11. The method of claim 1, wherein the positive ions comprise protons.

12. The method of claim 1, wherein the positive ions comprise carbon ions.

13. A method for performing computed tomography, the method comprising:

delivering a particle beam to an object, the particle beam comprising a plurality of positive ions;

measuring, with one or more particle detectors, a trajectory and an interaction quantity for individual positive ions in the particle beam that pass through the object;

generating a system of linear equations representing the passage of the individual positive ions through the object, the system of linear equations arranged in the form of Ax=b, where x is a discrete object vector representing an object parameter distribution, b is a vector that represents the measured interaction quantities of the individual positive ions resulting from interactions along their respective paths in the object, and A is a matrix comprising individual elements that correspond to an estimated intersection length of an individual positive ion in a corresponding voxel based at least in part on an estimated path through the object, wherein the system of linear equations has a plurality of solutions forming a set of feasible solutions;

determining, using one or more computer processors, a solution for the system of linear equations using a superiorization procedure comprising performing N superiorization cycles, each superiorization cycle comprising;

perturbing the discrete object vector x using a perturbation corresponding to a gradient of a merit function, wherein the merit function comprises a total variation of the object parameter distribution;

projecting the perturbed discrete object vector x using a perturbation resilient projection algorithm; and

replacing the discrete object vector x with the projected, perturbed discrete object vector x; and

generating a computed tomography image of the object based at least in part on the determined solution that comprises the discrete object vector x after the superiorization procedure has been performed,

wherein the number of cycles, N, is greater than or equal to 2.

14. A method for performing computed tomography, the method comprising:

delivering a particle beam to an object, the particle beam comprising a plurality of positive ions;

measuring, with one or more particle detectors, a trajectory and an interaction quantity for individual positive ions in the particle beam that pass through the object;

generating a system of linear equations representing the passage of the individual positive ions through the object, the system of linear equations arranged in the form of Ax=b, where x is a discrete object vector representing an object parameter distribution, b is a vector that represents the measured interaction quantities of the individual positive ions resulting from interactions along their respective paths in the object, and A is a matrix comprising individual elements that correspond to an estimated intersection length of an individual positive ion in a corresponding voxel based at least in part on an estimated path through the object, wherein the system of linear equations has a plurality of solutions forming a set of feasible solutions;

determining, using one or more computer processors, a solution for the system of linear equations using a superiorization procedure comprising performing N superiorization cycles, each superiorization cycle comprising;

perturbing the discrete object vector x using a perturbation corresponding to a gradient of a merit function;

projecting the perturbed discrete object vector x using a perturbation resilient projection algorithm; and

replacing the discrete object vector x with the projected, perturbed discrete object vector x; and

generating a computed tomography image of the object based at least in part on the determined solution that comprises the discrete object vector x after the superiorization procedure has been performed,

wherein the number of cycles, N, is less than or equal to 10.

15. The method of claim 14, wherein the number of cycles, N, is less than or equal to 6.

16. A method for performing computed tomography, the method comprising:

delivering a particle beam to an object, the particle beam comprising a plurality of positive ions;

measuring, with one or more particle detectors, a trajectory and an interaction quantity for individual positive ions in the particle beam that pass through the object;

generating a system of linear equations representing the passage of the individual positive ions through the object, the system of linear equations arranged in the form of Ax=b, where x is a discrete object vector representing an object parameter distribution, b is a vector that represents the measured interaction quantities of the individual positive ions resulting from interactions along their respective paths in the object, and A is a matrix comprising individual elements that correspond to an estimated intersection length of an individual positive ion in a corresponding voxel based at least in part on an estimated path through the object, wherein the system of linear equations has a plurality of solutions forming a set of feasible solutions;

forming M blocks from the measured data;

determining, using one or more computer processors, a solution for the system of linear equations using a superiorization procedure comprising performing N superiorization cycles, each superiorization cycle comprising;

perturbing the discrete object vector x using a perturbation corresponding to a gradient of a merit function;

projecting the perturbed discrete object vector x using a perturbation resilient projection algorithm, wherein projecting the perturbed discrete object vector x comprises projecting the perturbed discrete object vector x sequentially onto each of the M blocks prior to proceeding to the next cycle; and

replacing the discrete object vector x with the projected, perturbed discrete object vector x; and

generating a computed tomography image of the object based at least in part on the determined solution that comprises the discrete object vector x after the superiorization procedure has been performed,

wherein the number of cycles, N, is greater than or equal to 2.

17. A method for performing computed tomography, the method comprising:

delivering a particle beam to an object, the particle beam comprising a plurality of positive ions;

measuring, with one or more particle detectors, a trajectory and an interaction quantity for individual positive ions in the particle beam that pass through the object;

generating a system of linear equations representing the passage of the individual positive ions through the object, the system of linear equations arranged in the form of Ax=b, where x is a discrete object vector representing an object parameter distribution, b is a vector that represents the measured interaction quantities of the individual positive ions resulting from interactions along their respective paths in the object, and A is a matrix comprising individual elements that correspond to an estimated intersection length of an individual positive ion in a corresponding voxel based at least in part on an estimated path through the object, wherein the system of linear equations has a plurality of solutions forming a set of feasible solutions;

forming M blocks from the measured data;

determining, using one or more computer processors, a solution for the system of linear equations using a superiorization procedure comprising performing N superiorization cycles, each superiorization cycle comprising;

perturbing the discrete object vector x using a perturbation corresponding to a gradient of a merit function;

projecting the perturbed discrete object vector x using a perturbation resilient projection algorithm; and

replacing the discrete object vector x with the projected, perturbed discrete object vector x; and

generating a computed tomography image of the object based at least in part on the determined solution that comprises the discrete object vector x after the superiorization procedure has been performed,

wherein the number of cycles, N, is greater than or equal to 2; and

wherein the superiorization procedure comprises for each of the M blocks, repeating the superiorization cycle N times for each selected block.

18. A computed tomography system comprising:

a particle beam delivery system configured to deliver a particle beam to an object, the particle beam comprising a plurality of positive ions;

one or more particle detectors configured to measure a trajectory and an interaction quantity for individual positive ions that pass through the object;

computer hardware and a physical storage device, the physical storage device comprising instructions executable by the computer hardware, the instructions comprising;

first instructions to generate a system of linear equations representing the passage of the individual positive ions through the object, the system of linear equations arranged in the form of Ax=b, where x is a discrete object vector representing an object parameter distribution, b is a vector that represents the measured interaction quantities of the individual positive ions resulting from interactions along their respective paths in the object, and A is a matrix comprising individual elements that correspond to an estimated intersection length of an individual positive ion in a corresponding voxel based at least in part on an estimated path through the object, wherein the system of linear equations has a plurality of solutions forming a set of feasible solutions;

second instructions to determine a solution for the system of linear equations using a superiorization procedure comprising performing N superiorization cycles, each superiorization cycle comprising;

perturbing the discrete object vector x using a perturbation corresponding to a gradient of a merit function;

projecting the perturbed discrete object vector x using a perturbation resilient projection algorithm; and

replacing the discrete object vector x with the projected, perturbed discrete object vector x; and

third instructions to generate a computed tomography image of the object based at least in part on the determined solution that comprises the discrete object vector x after the superiorization procedure has been performed,

wherein the number of cycles, N, is greater than or equal to 2.

19. The system of claim 18, wherein the one or more particle detectors comprise:

upstream tracking detectors configured to measure data indicative of a spatial vector and location of a positive ion prior to passage through the object;

downstream tracking detectors configured to measure data indicative of a spatial vector and location of a positive ion after passage through the object; and

an energy detector configured to detect an energy of a positive ion after passage through the object.

20. The system of claim 19, wherein the one or more particle detectors are configured to rotate about an axis of the object.