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Introduction

3D Tomography for Art Conservation Franco Casali , Giuseppe Levi, Rosa Brancaccio, Matteo Bettuzzi , Maria Pia Morigi , Giovanni Marchetti. Introduction. The preservation of Italy’s vast cultural heritage is an intense, multi-disciplinary effort.

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Introduction

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  1. 3D Tomography forArt ConservationFranco Casali, Giuseppe Levi, Rosa Brancaccio, MatteoBettuzzi, Maria PiaMorigi, Giovanni Marchetti

  2. Introduction • The preservation of Italy’s vast cultural heritage is an intense, multi-disciplinary effort. • Parallel computing techniques for 3D tomography require expertise in Physics, Art, Computer Science • This presentation illustrates briefly what can be achieved. University of Bologna, est. 1088 AD

  3. 3D Tomography Radiographies Virtual Cuts Overview of Process

  4. What We’re Looking for

  5. Cone –Beam Tomography

  6. Sequence of Operations • Projections are the radiographs of the object at different angles • Collations & Cropping are often necessary because large objects are scanned in sections • Atenrads are projections corrected to take into account “dark image” noise and X-ray source field estimate. • Sinograms are images that contain all information to reconstruct 1 slice at fixed height

  7. Sequence of Operations • Reconstruction consists of 5 steps: • FFT of sinograms • Selection of 1 line in Fourier space • Filter line • Reverse FFT of filtered line • Reverse projection of result into normal space • The process is computation-intensive and complex, but each slice can be processed independently

  8. CT system with EBCCD An intensified camera collects the X-ray image on a 30 x 30 cm screen of scintillating material. The image represents the radiographic projection of the object.

  9. 3D CT of an Egyptian cat-shaped coffin X-ray parameters Emax 70 keV I 3 mA Exp 200 ms / view CT parameters Size: 37 x 10 x 20 cm Views: 360 Voxel: 600 m Detector: Ebccd ArchaeologicalMuseum - Bologna, Italy

  10. 3D CT of an Egyptian cat-shaped coffin 360 degrees radiographs ArchaeologicalMuseum - Bologna, Italy

  11. 3D CT of an Egyptian cat-shaped coffin 3D CT ArchaeologicalMuseum - Bologna, Italy

  12. 3D CT of an Egyptian cat-shaped coffin Virtual cuts ArchaeologicalMuseum - Bologna, Italy

  13. Globo ‘Danti’ (~1567) The Map Room (“Saladelle Carte Geografiche”) within Palazzo Vecchio, with the ancient large globe created by EgnazioDanti around 1567, on assignment of CosimoI de’ Medici, duke of Florence.

  14. Mosaic Cone-Beam CT Left: X-ray tube; Middle: the globe on aturntable; Right: detector and the translation axes (31000 radiographs).

  15. The CT system set-up Picture of the experimental set-up: the detector on the moving axes on the left, the globe on the rotating platform in the middle, and the tube on the vertical moving axis on the right.

  16. Digital projections 1 projection 360 projections Pixel size:  800 µm Pixel number for one projection: ~4500x280

  17. 14 slices in a planar projection Perspective distortions must be corrected before performing the CT reconstruction.

  18. 3D CT Reconstruction

  19. Top Structure

  20. Detail of the surface • Sectors of Hemp Cloth

  21. 2.30 m 78 cm 114 cm 1.14 m CT analysis of Kongo Rikishi (XIII century) Kongo Rikishi Wooden Japanese statue of Kamakura period (XIII century), in cypress wood (hinoki). Usingyosegi-zukuri (joined block) technique. Analysis carried out at "VenariaReale",Turin, Italy

  22. 29 31 30 32 CT analysis of Kongo Rikishi (XIII century)

  23. CT analysis of Kongo Rikishi (XIII century) Radiograph sequence Reconstruction: slice - section

  24. CT analysis of Kongo Rikishi (XIII century)

  25. Preliminary results Parallel Computing

  26. The Computational Challenge • Large artworks imply large amounts of data to be processed. • The KongoRikishi statue (over 2m tall) CT generated over 60 GB. The time needed for data processing was more than one month. • New means of reconstruction are needed.

  27. CT Reconstruction on a Windows HPC cluster • The problem lends itself to a parametric sweep approach because of data parallelism: • Cluster nodes execute same process on different projections, mostly asynchronously. • The analysis code was simply recompiled with Visual Studio 2008 • Analysis of the same images on a 5-node Windows HPC cluster achieved an impressive speedup (>20) compared to standard processing.

  28. 4CPU 4CPU 4 CPU 2 CPU 4CPU 4CPU CT Reconstructionon a Windows HPC cluster Dual core PC UNIBO HPC Cluster Preliminary results

  29. 4CPU 4CPU 4 CPU 4CPU 4CPU CT Reconstruction on a Windows HPC cluster • We are now designing a portable cluster that will let us do online, onsite data processing. • This will let us achieve better data quality. • Eventual DAQ errors can be corrected.

  30. Algorithm • The problem is inherently data-parallel • We can therefore exploit different levels of parallelism: • Data division among cluster nodes. • Parallel reconstruction on single node, via multi-threading on the node. • GPU acceleration on single node (image processing is mostly vectorial).

  31. Possible Cluster Architectures Corporate IT Infrastructure SystemsManagement Clients Monitoring AD DNS DHCP PublicNetwork Head Node Compute Node Compute Node Admin / User Cons Node Manager Node Manager WDS MPI MPI Job Scheduler Management MPI Management Management NAT PrivateNetwork MPINetwork Compute Cluster

  32. Typical for data-intensive applications Public Network Head Node Compute Node Compute Node Admin / User Cons Node Manager Node Manager WDS MPI MPI Job Scheduler Management MPI Management Management NAT PrivateNetwork SAN Storage System w. Cluster FS

  33. Further WorkWorkflow Automation • Typical workflow per image is highly repetitive, can be time-consuming. • Fast computing is just one aspect • What counts is time to analyzed result (from data to knowledge) Source: Microsoft Architecture Journal n.11, 2007

  34. Further WorkWorkflow Components Source: Microsoft Architecture Journal n.11, 2007

  35. Conclusion • High Performance Computing techniques open new possibilities in diagnosis and remedy of structural or material defects that develop over time in artworks. • Cost and effort can be contained by using familiar environments • Portability will greatly facilitate application where needed (ever tried to move a Doric column)?

  36. Questions & Answers

  37. Resources • X-ray Tomography Papers • http://www.xraytomography.com • Microsoft Architecture Journal, vol. 11, 2007 • http://msdn.microsoft.com/en-us/architecture/bb410935.aspx • D. L. Marks et al., “Cone-Beam Tomography with a Digital Camera”, Applied Optics 2001 vol.40 n.11 • D. A. Reimann et al., “Cone-Beam Tomography using MPI on Heterogeneous Workstation Clusters”, in Proceedings of the Second MPI Developers Conference, 1996

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