1 / 33

Simulation Study of Muon Scattering For Tomography Reconstruction

Explore muon scattering for tomography reconstruction using simulation studies presented at IEEE NSS-MIC 2009 in Orlando, Florida. Investigate concepts, algorithms, and results in the field.

mdovie
Download Presentation

Simulation Study of Muon Scattering For Tomography Reconstruction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. IEEE NSS-MIC 2009, Orlando, FL Simulation Study of Muon Scattering For Tomography Reconstruction • D. Mitra, A. Banerjee, S. White, • S. Waweru, R. Hoch • K. Gnanvo • M. Hohlmann Florida Institute of Technology

  2. IEEE NSS-MIC 2009, Orlando, FL Co-ordinates • Where are we?

  3. IEEE NSS-MIC 2009, Orlando, FL Cosmic Ray-generated Muons • more massive cousin of electron • produced by cosmic ray decay • arrives at sea-level @ 1 /cm2/min • highly penetrating, long half-life • affected by Coulomb force

  4. IEEE NSS-MIC 2009, Orlando, FL Muon Tomography Concept

  5. IEEE NSS-MIC 2009, Orlando, FL Muon Scattering Scattering angle Scattering function distribution: Approx. Normal (Bethe 1953) Heavy tail over Gaussian milirad 2 /cm

  6. IEEE NSS-MIC 2009, Orlando, FL Cosmic-ray generated Muon • Generated by proton and upper atmosphere’s interaction • Median at about 3 Gev • Peaks at about 30 degree

  7. CS Seminar, FIT Physics behind Models • Emission tomography: • SPECT • PET • MRI • Transmission tomography • X-ray • Some Optical • Reflection • Ultra Sound • Total Internal Reflection Fluoroscopy (TIRF) • Scattering/ Diffusion • Muon tomography • Some Optical tomography

  8. IEEE NSS-MIC 2009, Orlando, FL Experiment • GEANT4 simulation with partial physics for • scattering • Large array of Gas Electron Multiplier (GEM) • detector is being built • Poster# N13-246

  9. IEEE NSS-MIC 2009, Orlando, FL Reconstruction Algorithms • Point of Closest Approach (POCA) • Purely geometry based • Estimates where each muon is scattered • Max-Likelihood Expectation Maximization for Muon Tomography • Introduced by Schultz et al. (at LANL) • More physics based-model than POCA • Estimates Scattering density (λ) per voxel

  10. IEEE NSS-MIC 2009, Orlando, FL POCA Concept Incoming ray 3D POCA Emerging ray Three GEM detector-array above and three below

  11. IEEE NSS-MIC 2009, Orlando, FL POCA Result ≡ processed-Sinogram 40cmx40cmx20cm Blocks (Al, Fe, Pb, W, U) Unit: mm Θ U W Pb Fe Al

  12. IEEE NSS-MIC 2009, Orlando, FL POCA Discussion • Pro’s • Fast and efficient • Accurate for simple scenario’s • Con’s • No Physics: multi-scattering ignored • Deterministic • Unscattered tracks are not used

  13. IEEE NSS-MIC 2009, Orlando, FL ML-EM System Matrix L T Voxels following POCA track Dynamically built for each data set

  14. IEEE NSS-MIC 2009, Orlando, FL ML-EM Algorithm (adapted from Schultz et al., TNS 2007, & Tech Reports LANL) • gather data: (ΔΘ, Δ, p): scattering angles, linear displacements, momentums • estimate track-parameters (L, T) for all muons • initialize λ (arbitrary small non-zero number) • for each iteration k=1 to I (or, until λ stabilizes) • for each muon-track i=1 to M Compute Cij (2) for each voxel j=1 to N // Mj is # tracks (5) return λ

  15. IEEE NSS-MIC 2009, Orlando, FL ML-EM Reconstruction [In ‘Next Generation Applied Intelligence’ (Springer Lecture Series in Computational Intelligence: 214), pp. 225-231, June 2009.] • Very slow for complex scenario • Reconstruction used smart data structure for • speed and better memory usage

  16. IEEE NSS-MIC 2009, Orlando, FL POCA Result ≡ processed-Sinogram

  17. IEEE NSS-MIC 2009, Orlando, FL Slabbing Concept Slabbing Slice 3cm thick

  18. IEEE NSS-MIC 2009, Orlando, FL “Slabbing” studies with POCA:Filtered tracks with DOCA (distance of closest approach)Ev: 10MilVertical stack: Al-Fe-W: 50cm50cm20cm, Vert. Sep: 10cmSlab size: 3 cm

  19. IEEE NSS-MIC 2009, Orlando, FL POClust Algorithm: clustering POCA points Input: Geant4 output (list of all muon tracks and associated parameters) 1. For each Muon track { 2. Calculate the POCA pt P and scattering-angle 3. if (P lies outside container) continue; 4. Normalize the scattering angle (angle*p/3GeV). 5. C = Find-nearest-cluster-to-the (POCA pt P); 6. Update-cluster C for the new pt P; 7. After a pre-fixed number of tracks remove sporadic-clusters; 8. Merge close clusters with each-other } 9. Update λ (scattering density) of each cluster C using straight tracks passing through C Output: A volume of interest (VOI)

  20. IEEE NSS-MIC 2009, Orlando, FL POClust essentials • Not voxelized, uses raw POCA points • Three types of parameters: • Scattering angle of POCA point • proximity of the point to a cluster • how the “quality” of a cluster is affected by the new poca point and • merger of points or clusters • Real time algorithm: as data comes in

  21. IEEE NSS-MIC 2009, Orlando, FL POClust Results Medium: Air G4 Phantom U,W,Pb,Fe,Al Size: 40X40X20cm

  22. IEEE NSS-MIC 2009, Orlando, FL Three target vertical clutter scenario Al Fe W Al Fe Al-Fe-W: 40cm*40cm*20cm 100cm gap W

  23. IEEE NSS-MIC 2009, Orlando, FL Three target vertical clutter scenario:Smaller gap Al-Fe-W: 40cm*40cm*20cm 10cm gap Al Fe W

  24. IEEE NSS-MIC 2009, Orlando, FL POClust Results: Reverse Vertical Clutter Medium: Vacuum U Pb Al U-Pb-Al Size:40X40X20cm Gap:10cm

  25. IEEE NSS-MIC 2009, Orlando, FL POClust Results Medium: Vacuum U inside Pb box U size: 10X10X10cm Pb Box: 200X200X200 cm Thickness(Pb box): 10cm

  26. IEEE NSS-MIC 2009, Orlando, FL Why POClust & Not just POCA visualization? • Quantitate: ROC Analyses • Improve other Reconstruction algorithms • with a Volume of Interest (VOI) or • Regions of Interest (ROI) • Why any reconstruction at all? • POCA visualization is very noisy in a • complex realistic scenario

  27. IEEE NSS-MIC 2009, Orlando, FL Additional works with POClust • Clustering provides Volumes of Interest (VOI) inside the container: Run ML-EM over only VOI for better precision and efficiency • Slabbing, followed by Clustering • Clusters growing over variable-sized hierarchical voxel tree, followed by ML-EM • Automated cluster-parameter selection by optimization • 5. Use cluster λ values in a Maximum A Posteriori –EM, as priors (Wang & Qi: N07-6)

  28. IEEE NSS-MIC 2009, Orlando, FL POClust as a pre-processor Volume of Interest reduces after Clustering: A minimum bounding box (235cm X 235cm X 45cm) Initial Volume of Interest (400cm X 400cm X 300cm)

  29. EM after pre-processing with POClust • Scenario: 5 targets • VOI : 400X400X300 cm3 • Iterations: 50 • Targets: • Uranium (100,100,0), • Tangsten (-100, 100, 0) W U

  30. Results From EM over POClust generated VOI • Scenario: U, W, Pb, Al, Fe placed horizontally • Important Points: • IGNORE ALL VOXELS OUTSIDE ROI • EM COMPUTATION DONE ONLY INSIDE ROI After Clustering, VOI reduces, #Voxels = 18330 Here, Total Volume = 400 X 400 X 300 cm Voxel Size= 5 X 5 X 5 cm #Voxels = 384000

  31. IEEE NSS-MIC 2009, Orlando, FL A human in muon!…not on moon, again, yet … Twenty million tracks In air background 130cmx10cmx10cm Ca slab inside 150cmx30cmx30cm H2O slab GEANT4 Phantom

  32. IEEE NSS-MIC 2009, Orlando, FL Thanks! Debasis Mitra dmitra@cs.fit.edu Acknowledgement: Department of Homeland Security Domestic Nuclear Detection Office Acknowledgement: Patrick Ford for single handed heroic effort in maintaining the cluster

More Related