Type of presentation: Oral

IT-10-O-2946 Advanced 3-D Reconstruction Algorithms for Electron Tomography

Arslan I.1, Sanders T.2, Binev P.2, Roehling J. D.3, Batenburg K. J.4, Gates B. C.3, Katz A.5
1Pacific Northwest National Laboratory, Richland, WA, USA, 2University of South Carolina, Columbia, SC, USA, 3University of California-Davis, Davis, CA, USA, 4University of Antwerp, Antwerp, Belgium, and Centrum Wiskunde & Informatica, Amsterdam, The Netherlands., 5University of California–Berkeley, Berkeley, CA, USA
ilke.arslan@pnnl.gov

Electron tomography in the physical sciences has become a powerful tool for nanomaterial analysis. Recently, electron tomography is finding applications in more beam sensitive materials such as catalysts. For beam sensitive materials, the goal is to acquire the smallest number of images as possible but still maintain an accurate and high resolution 3-D reconstruction. Standard methods of 3D reconstruction, such as weighted back projection (WBP) and simultaneous iterative reconstruction technique (SIRT), are not equipped to handle this lack of information, and create significant blurring.  This gives rise to a search for new methods of reconstruction.  Two of the recent successful algorithms are the discrete algebraic reconstruction technique (DART) and total variation (TV) minimization within compressed sensing (CS).

 

DART uses an algebraic reconstruction method (e.g. ART, SIRT) and pairs it with the prior knowledge that there are only a small number (two or three) of different materials in the sample, each corresponding to a different gray value in the reconstruction.  An initial reconstruction is computed and rounded to the chosen fixed gray values based on some threshold, and iteratively refined using ART.  The method of TV minimization stems from the mathematical theory of compressed sensing and only recently became available due to new algorithms for solving the TV minimization problem.   The method considers the characterization of real images and encourages the reconstruction to minimize the number of jumps in gray values, creating clearer material boundaries than conventional methods (i.e. WBP or SIRT), hence creating a similar effect to that of DART.

 

The advantage of DART is that an accurate selection of the gray values and the rounding procedure for the reconstruction gives very accurate material boundaries, not available through any other reconstruction technique. However, the TV minimization procedure has fewer parameter selections, making initial reconstruction simpler and providing a more stable method for reconstruction. Moreover, the introduction of the TV norm has the potential for creating boundaries alternate to what a DART reconstruction would find.  Both methods are extremely valuable. In this presentation we discuss the pros and cons of each method, and show examples to illustrate when to use one method over the other.  


This research was funded in part by the DOE BES DE-SC0005822 and the LDRD and Chemical Imaging Initiative programs at PNNL. The Pacific Northwest National Laboratory is operated by Battelle under contract DE-AC05-76RL01830.