The microscopy research at the Bionanoprobe (currently at beamline 9-ID and later 2-ID after APS-U) of Argonne National Laboratory focuses on applying synchrotron X-ray fluorescence (XRF) techniques to obtain trace elemental mappings of cryogenic biological samples to gain insights about their role in critical biological activities. The elemental mappings and the morphological aspects of the biological samples, in this instance, the bacterium Escherichia coli (E. coli), also serve as label-free biological fingerprints to identify differently treated samples via fuzzy clustering. The key limitations of achieving good identification performance is the extraction of cells from raw XRF measurement via binary conversion, definition of features based on domain knowledge, dwell time and proportion of differently treated cells in the measurement.

Applications of X-ray computed tomography (CT) for porosity characterization of engineering materials often involve an extended data analysis workflow that includes CT reconstruction of raw projection data, binarization, labeling and mesh extraction. It is often desirable to map the porosity in larger samples but the computational challenge of reducing gigabytes of raw data to porosity information poses a critical bottleneck. In this work, we describe algorithms and implementation of an end-to-end porosity mapping code that processes raw projection data from a synchrotron CT instrument into a porosity map and visualization in the form of triangular face mesh. Towards this objective, we report the development of a novel subset reconstruction scheme for X-ray CT using filtered backprojection and a convolutional neural network that allows us to reconstruct arbitrarily-shaped subsets of a tomography object. We build upon this scheme to implement the complete code for porosity mapping. The code first detects possible voids by performing a coarse reconstruction on down-sampled projections and then improves the shape of those voids with higher detail offered by reconstructing selected subsets from the original raw data. We report measurements of the time taken by this code to perform complete processing from raw data to a triangular face mesh for several visualization scenarios on a single highperformance workstation equipped with GPU. We show that we can now visualize local porosity within a 8 gigavoxel CT volume (12 gigabytes raw data) within 1 to 2 minutes and a 64 gigavoxel CT volume (100 gigabytes of raw data) within 3 to 7 minutes.