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Simulation Study of Muon Scattering For Tomography Reconstruction

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. Co-ordinates. Where are we?. Cosmic Ray-generated Muons. more massive cousin of electron

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Simulation Study of Muon Scattering For Tomography Reconstruction

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  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

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