Algebraic Reconstruction of Ultrafast Tomography Images at the Large Scale Data Facility
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1 Algebraic Reconstruction of Ultrafast Tomography Images at the Xiaoli Yang, Thomas Jejkal, Rainer Stotzka, Halil Pasic, Tomy dos Santos Rolo, Thomas van de Kamp, Achim Streit Institute for Data Processing and Electronics, KIT University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association
2 Motivation for X-ray tomography ANKA Synchrotron light source at KIT: storage ring, beamline labs Topo-Tomo beamline: parallel X-ray beam, tomography experiment Studying moving biological samples in 3D: beetles, frog embryos Thomas van de Kamp, Tomy dos Santos Rolo, Julian Moosmann, Ralf Hofmann, Institute for Photon Science and Synchrotron Radiation (IPS)
3 Motivation for X-ray tomography ANKA Synchrotron light source at KIT: storage ring, beamline labs Topo-Tomo beamline: parallel X-ray beam, tomography experiment Studying moving biological samples in 3D: beetles, frog embryos Thomas van de Kamp, Tomy dos Santos Rolo, Julian Moosmann, Ralf Hofmann, Institute for Photon Science and Synchrotron Radiation (IPS)
4 Motivation for X-ray tomography ANKA Synchrotron light source at KIT: storage ring, beamline labs Topo-Tomo beamline: parallel X-ray beam, tomography experiment Studying moving biological samples in 3D: beetles, frog embryos Thomas van de Kamp, Tomy dos Santos Rolo, Julian Moosmann, Ralf Hofmann, Institute for Photon Science and Synchrotron Radiation (IPS)
5 Motivation for X-ray tomography ANKA Synchrotron light source at KIT: storage ring, beamline labs Topo-Tomo beamline: parallel X-ray beam, tomography experiment Studying moving biological samples in 3D: beetles, frog embryos Thomas van de Kamp, Tomy dos Santos Rolo, Julian Moosmann, Ralf Hofmann, Institute for Photon Science and Synchrotron Radiation (IPS)
6 Motivation for X-ray tomography ANKA Synchrotron light source at KIT: storage ring, beamline labs Topo-Tomo beamline: parallel X-ray beam, tomography experiment Studying moving biological samples in 3D: beetles, frog embryos Thomas van de Kamp, Tomy dos Santos Rolo, Julian Moosmann, Ralf Hofmann, Institute for Photon Science and Synchrotron Radiation (IPS)
7 Motivation for X-ray tomography ANKA Synchrotron light source at KIT: storage ring, beamline labs Topo-Tomo beamline: parallel X-ray beam, tomography experiment Studying moving biological samples in 3D: beetles, frog embryos Thomas van de Kamp, Tomy dos Santos Rolo, Julian Moosmann, Ralf Hofmann, Institute for Photon Science and Synchrotron Radiation (IPS)
8 Motivation for X-ray tomography ANKA Synchrotron light source at KIT: storage ring, beamline labs Topo-Tomo beamline: parallel X-ray beam, tomography experiment Studying moving biological samples in 3D: beetles, frog embryos Thomas van de Kamp, Tomy dos Santos Rolo, Julian Moosmann, Ralf Hofmann, Institute for Photon Science and Synchrotron Radiation (IPS)
9 Motivation of sparse reconstruction Ultrafast tomography system: continuous rotation, high speed Minimizing the effects of movements on reconstruction accuracy Lower radiation dose longer lifetime for scientific studying projections in [0-180 ]:insufficient data for exact image reconstruction Reconstruction Standard reconstruction method: filtered-back projection (FBP) artifacts Better reconstruction algorithm needed Sparse reconstruction technique based on Total Variation (TV): accurate signal restoration of incomplete data
10 CS-ART (Compressive Sampling-based Algebraic Reconstruction Technique) Precise forward model: geometry of detectors TVAL3* X-ray line: width, distance Equations system: p=as Precise reconstruction Solver of linear inverse problem with TV minimization Promising image reconstruction, fast convergence rate *TVAL3 C. Li, W. Yin and Y. Zhang, User s guide for TVAL3: TV minimization by augmented lagrangian and alternating direction algorithms,
11 Reconstruction images CS-ART algorithm produces high quality images using only a few projections. Standard: 1500 projections (reference) (a) reference (b) (c) Data reduction High quality image (image segmentation) Parallelization required Standard: 60 projections CS-ART: 60 projections Computing expensive Thousand parallel slices: 10 min (standard), tens of hours (CS-ART)
12 LSDF & Parallel computing LSDF: at KIT for data intensive scientific experiments Storage facility Data storage, management and access Computing cluster with 58 nodes Data computing and analysis in parallel Online Storage Computing & Analysis Data parallel computing Each slice: MATLAB software, independently 1024 slices mapping task Hadoop cluster (computer nodes)
13 Parallel computing performance Measured time and speedup factor Speedup min High computing performance achieved 330 seconds (less than 6 min), speedup 120 Enable the application of novel algorithm for ultrafast tomography Workflow of LSDF in application of ANKA
14 Conclusions CS-ART algorithm shows high potential to reconstruct better images for reduced data. The promising reconstruction result and high computing performance at LSDF greatly support the construction of the ultrafast tomography system at ANKA. Workflow of LSDF will enhance the data storage, management and automatic data intensive computing for ANKA. Workflow of LSDF is not limited to ANKA. It also supports other institutes involving data intensive computing (micro-tomography and other tomography experiments)
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