“Live” Tomographic Reconstructions
Alun Ashton
Mark Basham
IncentiveHigher resolution cameras are forcing larger collection
times.• I12 at DLS will use a 4000x2500 pixel detector (PCO
4000) capable of approximately 2 frames per second (Technically 4-5 frames). Assuming the best case scenario this will require around 30-40 minutes to collect the data(~4000 images i.e. the width of the detector).
• Even with good cluster performance, it could still take around 30 minutes to make the 80GB reconstructed volume (4000x4000x2500)
Having to wait an hour from start to finish before being able to see what has been imaged is frustrating, and could waste beam time due to misalignments etc.
Trying something different
• Recent work has focused on fast and accurate reconstructions.– Make good use of complete data sets to correct for
various anomalies.– This has to happen after the collection has been
completed (on an 80Gb file…)• This work focuses on providing a reconstruction during
the data collection.– Less quality, and smaller reconstructions (e.g.
1000x600) to allow visualisation.– Gives the user the ability to see the volume after only
a few minutes.
Traditional Tomography
Ingoing Path
Outgoing Path
Direction of the beam
Traditional TomographyEach new acquisition
collects the next segments data
Traditional Tomography
Traditional Tomography
Traditional Tomography
Traditional TomographyAll the data has been collected
and the final full size and quality reconstruction
can be produced
Experimental changes
• To make the maximum use of this methodology there needs to be some experimental changes.
• The main change is in the way that the data is collected
• This requires certain hardware requirements mainly a sample stage capable of continuous rotation
• The acquisition is then preformed as follows
Continuous rotation
Ingoing Path
Outgoing Path
Continuous rotationEach new
acquisition skips 4 segments and then collects the
5th
Continuous rotation
Continuous rotationInitial lowest
quality reconstruction
can now be calculated
Continuous rotationBecause its
transmition you can flip the image and then its going the right way…. So becomes red
Continuous rotation
Continuous rotation
Continuous rotationRefined
reconstruction can now be
calculated and replaces the
previous one.
Continuous rotation
This is where skipping 4 becomes important.
Continuous rotation
Continuous rotationRefined
reconstruction can now be
calculated and replaces the
previous one.
Continuous rotation
Continuous rotation
Continuous rotation
Continuous rotationRefined
reconstruction can now be
calculated and replaces the
previous one.
Continuous rotation
Continuous rotation
Continuous rotationAll the data has been collected
and the final full size and quality reconstruction
can be produced
Progressive Collection
• Advantages– A full reconstruction can be preformed after only a
fifth of the acquisition time, albeit at reduced resolution.
– As there is a gap between acquisitions, the full sector can be integrated, then the gap can be used for camera readout.
• Disadvantages– Requires custom software to reconstruct, or convert
to classical data.– If the gap is too large, acquisition time can be
increased.
Integration Time
Sector 1 Sector 2
S1 S2
Traditional
New
Acquisition
Detector tomemory
Acquisition
Detector tomemory
Time
User
Save Images to central Storage
Request Aquisition
Request and Retrieve Images
Request Data from storage
Request full Reconstruction
Display ‘live’ data
Process data and produce live volumes
Process all the data and create the full
volume reconstructions
Display the full data
Architecture
Beamline Control PCCamera Control and Live
Reconstruction PC
Central StorageCluster Computing
Resources
Digital camera
User
Save Images to central Storage
Request Aquisition
Request and Retrieve Images
Request Data from storage
Request full Reconstruction
Display ‘live’ data
Process data and produce live volumes
Process all the data and create the full
volume reconstructions
Display the full data
Architecture
Start-up
User
Save Images to central Storage
Request Aquisition
Request and Retrieve Images
Request Data from storage
Request full Reconstruction
Display ‘live’ data
Process data and produce live volumes
Process all the data and create the full
volume reconstructions
Display the full data
Architecture
Start-up
User
Save Images to central Storage
Request Aquisition
Request and Retrieve Images
Request Data from storage
Request full Reconstruction
Display ‘live’ data
Process data and produce live volumes
Process all the data and create the full
volume reconstructions
Display the full data
Architecture
Collect Images
User
Save Images to central Storage
Request Aquisition
Request and Retrieve Images
Request Data from storage
Request full Reconstruction
Display ‘live’ data
Process data and produce live volumes
Process all the data and create the full
volume reconstructions
Display the full data
Architecture
End of collection
User
Save Images to central Storage
Request Aquisition
Request and Retrieve Images
Request Data from storage
Request full Reconstruction
Display ‘live’ data
Process data and produce live volumes
Process all the data and create the full
volume reconstructions
Display the full data
Architecture
Produce full reconstruction
User
Save Images to central Storage
Request Aquisition
Request and Retrieve Images
Request Data from storage
Request full Reconstruction
Display ‘live’ data
Process data and produce live volumes
Process all the data and create the full
volume reconstructions
Display the full data
Differences to normal setups
Computing Hardware for the live reconstruction
• Standard PC server– Passes the data on to the central storage– Scales and applies the flat field correction to the
images as they come in.– Runs the Host program for the Tesla
• Tesla Graphics Processor Unit– Takes the scaled and corrected images– Filters the images.– Provides the Back Projection.
Why the TESLA
• Tomography is inherently very parallelisable– Tesla requires around 100,000 concurrent threads to
make it effective– This then allow for in general a single GPU to run 40
times faster on these problems than a single CPU or 10 times faster then a QuadCore.
• Space and power are saved in this case as a 1U Tesla Unit can effectively replace 20 dual processor quad core machines, for tomographic reconstruction.
• This also allows our beamline machine to pack the punch required to make the ‘live’ reconstructions possible.
Conclusions
• This methodology for collecting tomographic data should give the users much more insight into there samples and more time to make decisions about collections.
• The Tesla GPU is a good way of increasing the speed of Tomographic reconstruction, and has been proven in various different labs around the world.
• We can modify the way in which the experiment is preformed to make the most use or influence the choice of hardware, such as the modifications made to allow for camera readout and continuous rotation stage.
Acknowledgements
• Manchester University– Valeriy Titarenko, Albrecht Kyrieleis, Phil Withers,
Mark Ibson.• Diamond Light Source
– Michael Drakopoulos, Thomas Connolley• Architecture
– Piercarlo Grandi, Nick Rees, Bill Pullford
• Mark Basham, [email protected]