“Live” Tomographic Reconstructions Alun Ashton Mark Basham

Overview Incentive Experimental Changes Beamline Overview Reconstruction Hardware Conclusions

Incentive Higher 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 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 Tomography Each new acquisition collects the next segments data

Traditional Tomography

All 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 rotation Each new acquisition skips 4 segments and then collects the 5th

Continuous rotation

Initial lowest quality reconstruction can now be calculated

Continuous rotation Because its transmition you can flip the image and then its going the right way…. So becomes red

Continuous rotation

Refined reconstruction can now be calculated and replaces the previous one.

Continuous rotation This is where skipping 4 becomes important.

Continuous rotation

Refined reconstruction can now be calculated and replaces the previous one.

Continuous rotation

Refined reconstruction can now be calculated and replaces the previous one.

Continuous rotation

All 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 1Sector 2 S1S2 Traditional New Acquisition Detector to memory Acquisition Detector to memory Time

Architecture Beamline Control PC Camera Control and Live Reconstruction PC Central Storage Cluster Computing Resources Digital camera

Architecture Start-up

Architecture Start-up

Architecture Collect Images

Architecture End of collection

Architecture Produce full reconstruction

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,