IMAGING AND SENSING BASED ON MUON TOMOGRAPHY

• US 20080315091A1
• Filed: 04/23/2008
• Published: 12/25/2008
• Est. Priority Date: 04/23/2007
• Status: Active Grant

• Current Assignee: TRIAD National Security, LLC
• Original Assignee: Los Alamos National Security LLC (Government of the United States of America)
• Inventors: Smith, Richard A., Borozdin, Konstantin N., Sossong, Michael James, Morris, Christopher L., Schultz, Larry Joe, Saunders, Alexander, Vineyard, David C., Scoggins, Gary E., Green, J. Andrew, Klugh, David C., Colthart, James M., Hengartner, Nicolas W.
• Granted Patent: US 8,288,721 B2
• Time in Patent Office: Days
• US Class Current: 250/307
• CPC Class Codes: G01T 1/18 with counting-tube arrangements, e.g. with Geiger counters tubes H01J47/08; with alarm provision G01T7/125
• CPC Class Codes: G01T 3/00 Measuring neutron radiation G01T5/00 takes precedence
• CPC Class Codes: G01V 5/0016 Active interrogation, i.e. using an external radiation source, e.g. using pulsed, continuous or cosmic rays
• CPC Class Codes: G01V 5/0025 Measuring scattered radiation
• CPC Class Codes: G01V 5/005 using Tomography, e.g. CT or SPECT detector details in CT applications G01T1/2985
• CPC Class Codes: G01V 5/22 Active interrogation, i.e. by irradiating objects or goods using external radiation sources, e.g. using gamma rays or cosmic rays
• CPC Class Codes: G01V 5/222 measuring scattered radiation
• CPC Class Codes: G01V 5/226 using tomography

Abstract

Techniques, apparatus and systems for detecting particles such as muons for imaging applications. Subtraction techniques are described to enhance the processing of the muon tomography data.

Claims

1. A method for using natural cosmic muons as a radiation source to obtain muon tomographic images of an object, a cargo or vehicle under inspection, comprising:

obtaining one or more cosmic muon images of an object, cargo or vehicle under inspection;

subtracting a reference cosmic muon image of a reference object, cargo or vehicle that represents a background of the object, cargo or vehicle under inspection from an obtained cosmic muon image of the object, cargo or vehicle under inspection to obtain a subtracted image; and

processing the subtracted image to determine whether the object, cargo or vehicle under inspection contains a target object that is absent from the reference cosmic muon image.

2. The method as in claim 1, comprising:

using a distribution of atomic number Z in the obtained muon tomographic images and a distribution of atomic number Z in the reference cosmic muon image to detect presence or absence of the target object.

3. The method as in claim 1, wherein:

the reference cosmic muon image is based on averaging reconstructed values of each voxel in muon tomography imaging data.

4. The method as in claim 1, wherein:

the reference cosmic muon image is based on a median reconstructed value of reconstructed values of each voxel in muon tomography imaging data.

5. The method as in claim 1, wherein:

the reference cosmic muon image is based on a predetermined percentile value of reconstructed values of each voxel in muon tomography imaging data.

6. The method as in claim 1, wherein:

the predetermined percentile value is the 95^th percentile value.

7. The method as in claim 1, wherein:

the reference cosmic muon image is constructed based on prior knowledge on the object, cargo or vehicle under inspection.

8. The method as in claim 1, wherein:

obtaining one or more cosmic muon images of the object, cargo or vehicle under inspection comprises;

obtaining predetermined charged particle tomography data corresponding to scattering angles and estimated momentum of cosmic muons passing through the object, cargo or vehicle under inspection;

providing a probability distribution of muon scattering for use in an expectation maximization (ML/EM) algorithm, the probability distribution being based on a statistical multiple scattering model;

determining substantially maximum likelihood estimate of object volume density using the expectation maximization (ML/EM) algorithm; and

outputting a reconstructed object volume scattering density of the object, cargo or vehicle under inspection.

9. A method of inspection using muon tomography, comprising:

scanning a vehicle or cargo container using muon sensors to obtain muon tomography imaging data;

creating a reconstruction of the vehicle or cargo container from the muon tomography imaging data;

subtracting a reconstruction of a known background model for the vehicle or cargo container from the reconstruction of the vehicle or cargo container to obtained a subtracted image; and

analyzing the subtracted image to detect whether a target object is present in the vehicle or cargo container.

10. The method as in claim 9, wherein:

the know background model or the vehicle or cargo container is obtained based on a statistical method.

11. A method of performing inspection based on muon tomography, comprising:

scanning a subject using muon sensors to obtain muon tomography image data;

creating a digital reconstruction of the subject from the muon tomography image data; and

comparing the digital reconstruction of the subject to digital reconstructions of objects having known shapes to determine whether a target object is present in the subject.

12. A method of creating digital reconstructions using muon sensor data for a scanned object, comprising:

determining a closest approach point between incoming and outgoing trajectories of muons;

determining a closest approach distance between incoming and outgoing trajectories of the muons;

determining the angle between the incoming and outgoing trajectories; and

calculating the thickness of the material using the covariance of the angle and the closest approach distance.

13. A muon tomography system, comprising:

a first set of position sensitive muon detectors located on a first side of an object holding area to measure positions and directions of incident muons towards the object holding area;

a second set of position sensitive muon detectors located on a second side of the object holding area opposite to the first side to measure positions and directions of outgoing muons exiting the object holding area; and

a signal processing unit to receive data of measured signals of the incoming muons from the first set of position sensitive muon detectors and measured signals of the outgoing muons from the second set of position sensitive muon detectors, the signal processing unit configured to analyze scattering behaviors of the muons caused by scattering of the muons in the materials within the object holding area based on the measured incoming and outgoing positions and directions of muons to obtain a tomographic profile or the spatial distribution of scattering centers within the object holding area,

wherein the signal processing unit is operable to subtract a reference cosmic muon image that represents a background of an object, cargo or vehicle under inspection in the object holding area from an obtained cosmic muon image of the object, cargo or vehicle under inspection to obtain a subtracted image; and

process the subtracted image to determine whether the object, cargo or vehicle under inspection contains a target object that is absent from the reference cosmic muon image.

14. The system as in claim 13, wherein:

the signal processing unit uses a distribution of atomic number Z in the obtained muon tomographic images and a distribution of atomic number Z in the reference cosmic muon image to detect presence or absence of the target object.

15. The system as in claim 13, wherein:

the reference cosmic muon image is based on averaging reconstructed values of each voxel in muon tomography imaging data.

16. The system as in claim 13, wherein:

the reference cosmic muon image is based on a median reconstructed value of reconstructed values of each voxel in muon tomography imaging data.

17. The system as in claim 13, wherein:

the reference cosmic muon image is based on a predetermined percentile value of reconstructed values of each voxel in muon tomography imaging data.

18. The system as in claim 17, wherein:

the predetermined percentile value is the 95th percentile value.

19. The system as in claim 13, wherein:

the reference cosmic muon image is constructed based on prior knowledge on the object, cargo or vehicle under inspection.