COMPUTERIZED IMAGE RECONSTRUCTION METHOD AND APPARATUS

• US 20150030227A1
• Filed: 02/01/2013
• Published: 01/29/2015
• Est. Priority Date: 02/01/2012
• Status: Active Grant

• Current Assignee: The Research Foundation for The State University of New York (State University of New York)
• Original Assignee: The Research Foundation for The State University of New York (State University of New York)
• Inventors: Liang, Zhengrong, Ma, Jianhua, Liu, Yan
• Granted Patent: US 9,251,606 B2
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: G06T 11/005 Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
• 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: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10124 Digitally reconstructed radiograph [DRR]
• CPC Class Codes: G06T 2207/30096 Tumor; Lesion
• CPC Class Codes: G06T 2211/424 Iterative

Abstract

Provided herein is a computerized image reconstruction apparatus and method that includes recording projection path information and energy loss information of a plurality of particles traversing an object being imaged and determining an estimated image of the object based on the projection path information and the energy loss information sampled into a projection format. The estimated image includes an active volume defined by an enclosure border. Cost function minimization uses an Adaptive-weighted Total Variation cost function, a Penalized Weighted Least-Squares cost function, or an Alpha-Divergence cost function to update the estimated image. An iterative updating algorithm corresponding to the cost function updates the estimated image and produces a final image based on the estimated image according to a predetermined threshold.

Claims

1. A method for imaging an object, the method comprising:

recording projection path information and energy loss information of a plurality of particles traversing an object;

determining an estimated image based on the projection path information and the energy loss information sampled into a projection format, wherein the estimated image includes an active volume defined by an enclosure border;

updating the estimated image by performing cost function minimization using one of an Adaptive-weighted Total Variation (AwTV) cost function, a Penalized Weighted Least-Squares (PWLS) cost function, and an Alpha-Divergence (α

-D) cost function;

updating the estimated image by performing an iterative updating algorithm corresponding to the cost function minimization; and

when a difference between the updated estimated image and a previously estimated image is lower than a predetermined threshold, producing a final image based on the estimated image,

wherein the one of the AwTV cost function, the PWLS cost function, and the α

-D cost function is performed based on the estimated image, the recorded projection path information and energy loss information and a volumetric path.

2. The method of claim 1, wherein the step of determining the estimated image comprises:

sampling the recorded projection path information and energy loss information to create a tomographic projection;

reconstructing an initial image by applying a Filtered Back-Projection (FBP) image reconstruction method using the sampled recorded projection path information and energy loss information;

identifying the enclosure border of the initial image; and

designating a volume within the enclosure border as the active volume of the object being imaged.

3. The method of claim 1, wherein the step of performing the iterative updating algorithm corresponding to the cost function minimization comprises:

performing a first iteration by updating the volumetric path based on an estimated path within the active volume, computing expected data for projection path information and energy loss information using the updated volumetric path and the estimated image, performing the cost function based on computed expected data and recorded data, and obtaining an updated object image; and

performing subsequent iterations by updating the estimated active volume based on the updated object image, updating the volumetric path based on the updated active volume and the updated object image, minimizing the cost function based on the updated active volume, updated volumetric path, updated object image, the computed expected data and the recorded data, to further update the object image.

4. The method of claim 1, wherein the projection path information includes energy loss information of each of the plurality of particles, position information, direction information, orientation information, and angle information of the plurality of particles, upon entering and exiting the object, object parameter distribution and interaction quantity information.

5. The method of claim 1, wherein the volumetric path is a banana-shaped volume determined from recorded projection path information and energy loss information within the active volume,

wherein the banana-shaped volume provides an object parameter distribution including an all path probability distribution, and

wherein a centerline of the banana-shaped volume indicates a most likely path of the plurality of particles.

6. The method of claim 1, wherein the iterative updating algorithm comprises one of:

a projection onto a Plurality Of Convex Sets (POCS) algorithm for minimizing the AwTV cost function;

a Gauss-Seidel (GS) updating algorithm for minimizing the PWLS cost function; and

an α

D-TV minimizing algorithm for minimizing the α

-D cost function.

7. The method of claim 1, wherein the AwTV cost function is determined by:

min<sub>η

≧

0</sub>∥

η

∥

_AWTV,

wherein the AwTV cost function is subject to an error tolerance condition ε

, represented by;

∥

p−

Hη

∥

₂²≦

ε

,and

wherein η

is a vector of a relative electron density distribution within the object, p is the recorded energy loss information, and ε

is an error tolerance data fidelity constraint determined by a noise level of p, H is an integration operator for computing an expected energy-loss information along a projection path, and Hη

is an expected value of a recorded value p expressed as an integral of energy loss along the estimated path through the estimated object image within the estimated active volume at the n-th iteration.

8. The method of claim 7, wherein the AwTV cost function is determined by:

9. The method of claim 1, wherein the AwTV cost function is expanded to a TV-Stroke cost function represented by:

10. The method of claim 1, wherein the α

-D cost function is represented by;

11. The method of claim 1, wherein the PWLS cost function is represented by:

Φ

(η

)=(p−

Hη

)^TΣ

<sup>−

1</sup>(p−

Hη

)+γ

Q(η

),

wherein Hη

is an expectation of the measured proton energy loss p, γ

is a smoothing parameter which controls a degree of agreement between the estimated and the recorded projection path information, Σ

is a diagonal matrix with a diagonal value being a corresponding variance of a data element of p, and Q(η

) is a penalty term solved by the AwTV cost function.

12. The method of claim 1, wherein the particles traversing the object are one of protons, neutrons, muons, carbon and alpha particles.

13. The method of claim 1, wherein the plurality of particles includes X-ray photons, Gamma-ray photons, and wherein the recorded projection path is a straight line.