Tomography data processing and management
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1 Tomography data processing and management P. Cloetens X ray Imaging Group, ESRF Slide: 1
2 Computed Tomography Xenopus tropicalis (OsO4 fixation) Motivation complex materials 3D microscopy representative elementary volume input for calculations: µ structure properties 'non destructive' in situ experiments ( dynamics, mechanics, ) Compromise spatial resolution micron 100 nm field of view millimeter, centimeter N Pollet (ibaic,u psud), R Boistel (namc,u psud), P Cloetens Slide: 2
3 Computed Tomography Xenopus tropicalis Current situation: data acquisition (OsO4 fixation) data processing (reconstruction and pre processing) data storage / transfer Grid opportunities: data analysis 'on line' data processing large data sets data transfer sophisticated methods N Pollet (ibaic,u psud), R Boistel (namc,u psud), P Cloetens Slide: 3
4 Synchrotron based tomography the whole object Parallel beam case... is imaged at once, slices are independent New parameters: time, in situ experiments distance: holotomography, 3DXRD energy: edge CT, XANES, fluorescence } angles are independent Slide: 4
5 Micro tomography work flow Data acquisition ~ 1 second 2 hours Data processing (pre processing and 3D reconstruction) 1 hour 6 months Data analysis (extracting quantitative parameters, simulations) 1 month 3D Growth Law J. Lambert et al, PRL 99, (2007) Slide: 5
6 Data acquisition FReLoN (Fast Read out Low Noise) CCD (2k x 2k camera, '80 Mpixels/s', '160 MB/s') SPEC control (same macros on ID19, ID22NI, BM05,...; Bliss) but Device Servers for camera,... Highly optimized Step by step acquisition or Continuous acquisition Storage: directly to NICE file servers over 1Gb / 10 Gb link normal scans and network OK local buffer on fast disks fast tomography (~70 MB/s 120 MB/s) Raw data: 16 GB / sample (64 GB for holo tomography) 1 TB / day in user experiment is feasible Slide: 6
7 Data processing PyHST (SciSoft, A Mirone, C Ferrero) + (CS, G Förstner) Filtered back projection implementation Optimized for total through put not for a single scan parallelization in 1 GB sub volumes not individual slices 20 dedicated cpu's ( common cpu's ) Improved through put by including several pre processing steps: motion correction, basic ring correction, hot pixel correction, distortion correction,... User interacts with Octave + ImageJ and terminal Special pre processing with Octave (phase retrieval) or Matlab/IDL Volume reconstructed data raw data! (no data reduction) Slide: 7
8 edge detection versus holography towards Fraunhofer diffraction (Fresnel diffraction) D = 0.15 m D = 3.1 m each edge imaged independently access to phase, if recorded at D s no access to phase, only to borderλ = 0.7 Å defocused image D a 50 µm D a Slide: 8
9 Holo tomography 1) phase retrieval with images at different distances D Phase map 2) tomography: repeated for ~ 1000 angular positions 3D distribution of or the electron density improved resolution straightforward interpretation processing PS foam P.Cloetens et al., Appl. Phys. Lett. 75, 2912 (1999) JP Guigay, M Langer, R Boistel, P Cloetens, Opt. Lett., 32, 1617 (2007) Slide: 9
10 Data processing: limitations on parallelization Data Input Output (file servers) Too many reconstruction jobs blocks processing and acquisition (!) Maximum 20 PyHST jobs on dedicated cluster cronus (no flocking) Memory intensive jobs (holo tomography) Only 4 or 8 GB / physical machine on NICE > 1 2 GB / CPU Performance collapses on swapping In practice: only 8 GB machines used, 1 job per physical machine Slide: 10
11 Data storage / transfer Tomography produces huge amounts of data (raw + reconstructed) Short term: NICE file servers Case ID19 (~ 80 TB) inhouse: 8TB (87% full) paleo: 8TB (83% full) inhouse1: 8TB (85% full) lamino: 4TB (99% full) external: 8.5 TB (83% full) graintracking: 7 TB (99% full) external1: 6 TB (89% full) external2: 6 TB (82% full) Medium term: 6 months central backup on tape many data restorations from central backup (man power) Long term: external hard disks / typically only reconstructed data NICE also used for long term storage in practice (congestion) Slide: 11
12 Database TomoDB II database (S. Schulze, MIS) Most experimental conditions stored Not all data processing parameters Only available through intranet Slide: 12
13 Data Flow Parameters courtesy: T. Weitkamp Slide: 13
14 Grid opportunities: data analysis From simple segmentation over parameter extraction (local thickness, volume, ) to simulations (finite elements, finite differences,...) Presently left to user Several dispersed tools (VGStudio, Aphelion, ImageJ, Libido,...) Faster data analysis can significantly increase publication rate Faster data analysis can significantly decrease data storage Users could perform their analysis with ESRF resources Users can make specialized code available Slide: 14
15 Grid opportunities: 'on line' data processing Presently: on line reconstruction of sample slice On line reconstruction of volumes multi scale approach (e.g. zoom tomography) in situ experiments (result defines what to do next) Avoid processing time >> acquisition time extended stay of user post processing by user or beamline staff Slide: 15
16 Grid opportunities: large data sets Nano imaging: improve spatial resolution with reasonable field of view increase number of pixels, scales as N3 CCD's used today are only 2K x 2K 2K x 2K 16 GB 4K x 2K 64 GB 4K x 4K 128 GB Projection within the Upgrade Program: 8K x 8K 1 TB for a single scan! Slide: 16
17 Grid opportunities: data transfer and archiving External hard disk solution is impractical and not secure Reliable and fast data transfer over network Long term archiving for inhouse data is under discussion Slide: 17
18 Grid opportunities: sophisticated methods Nano scale imaging: CDI (Coherent Diffraction Imaging) Projection Microscopy Very computer intensive methods (CPU power, memory usage) Slide: 18
19 The Nano Imaging end station ID22NI Projection Microscopy PXM electron density Dose efficient, fast ; magnified holotomography Scanning Transmission Microscopy STXM X ray Fluorescence: element distribution Slow ; trace elements Slide: 19
20 Grid opportunities: Iterative Phase Retrieval Fluotomography Chemical identification Magnified holotomography Electron density mapping CPU bound Iterative minimization of cost functional Slide: 20
21 Conclusions Tomography is a three steps process: data acquisition, data processing (reconstruction) and data analysis. The amount of data involved is huge. There is no data reduction before the data analysis. Parallelization is straightforward (independent slices or angles). It is limited in practice by data input/output. Possible grid opportunities: data analysis data transfer more sophisticated methods of pre processing, reconstruction Slide: 21
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