| AVS 63rd International Symposium & Exhibition | |
| Applied Surface Science | Tuesday Sessions |
| Session AS+SS-TuA |
| Session: | Data Analytics in Surface Science and Nanoscience |
| Presenter: | Peter Cumpson, Newcastle University, UK |
| Authors: | P.J. Cumpson, Newcastle University, UK I.W. Fletcher, Newcastle University, UK N. Sano, Newcastle University, UK A.J. Barlow, Newcastle University, UK |
| Correspondent: | Click to Email |
Multivariate analysis offers the exciting prospect of unlocking the information content of 3D SIMS of complex organic and biological samples with sub-micron resolution. However applying principal component analysis (PCA) to large images or 3D imaging depth-profiles has been difficult until now because of the Gb to Tb size of the matrices of data involved. The result has always been an "out of memory" error.
Recently[1] we applied two algorithms, RV1 and RV2, originally developed by Halko et al[2] that improve the speed of PCA and allow datasets of unlimited size respectively, even on ordinary personal computers. In this presentation we show results of applying these algorithms to perform PCA on full 3D ToF-SIMS data of several examples of plant and small animal tissue. The datasets we process in this way are typically 128x128 or 256x256 pixel depth-profiles of around 100 layers, each voxel having a 70,000 value mass spectrum associated with it, giving datasets of at least 1Tb in size when uncompressed. These data were acquired using our Ionoptika J105 and Iontof IV instruments, with Helium Ion Microscope images of particular key features.
Even for such large datasets a rapid PCA calculation is often needed during analysis sessions to inform decisions on the next analytical step. We have therefore implemented the RV1 algorithm on a PC having a Graphical Processor Unit (GPU) card containing 2,880 individual processor cores[3]. This increases the speed of calculation by a factor of around 4 compared to what is possible using the fastest commercially-available desktop PCs, and full PCA is now performed in less than 7 seconds.
We then use the GPU to allow real-time interactive visualization of the principal components in 3D. This leads to some spectacular and information-rich tomographic images that can be an excellent basis for discussion between analysts and the biologists and medics who understand the morphology and anatomy of their tissue samples.
[1] P J Cumpson et al, Surf. and Interface Anal. 47 (2015) 986-993.
[2] N P Halko et al, SIAM Review,Survey Rev. Sec. 53 (2011) 217–288.
[3] P J Cumpson et al, Surf. and Interface Anal., onlinelibrary.wiley.com/doi/10.1002/sia.6042/full