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Kallman, Jeffrey S.

Publications and source records attributed to Kallman, Jeffrey S..

Livermore tomography tools: Accurate, fast, and flexible software for tomographic science

Livermore Tomography Tools (LTT) is a customizable scientific software package that enables a broad range of research and development efforts into computed tomography (CT). Here, it was developed to process x-ray and neutron CT data accurately and rapidly from raw detector counts to reconstructed volumes with the flexibility to handle many special cases. LTT fulfills long-term CT software goals to provide quantitatively accurate results reported in physical units (e.g., mm -1 or cm -1 ) while exploiting all available computational advantages to maximize speed. Written in C/C++ with support for multiple CPUs and GPUs, LTT runs on many computing platforms (Linux/Unix, Windows, and Mac; laptops to supercomputers). As a result, LTT can:process data acquired from various custom-built and commercially available CT scanners, model and simulate x-ray and neutron interactions to encourage algorithm prototyping, and allow for rapid insertion of the latest algorithms.We describe LTT’s software architecture, user interfaces, and its 88 algorithms (as of this writing) for pre-processing, reconstruction, post-processing, and simulation that support many scanner geometries (parallel-, fan-, cone-beam, and custom). Several applications are presented that illustrate LTT’s accuracy, speed, and flexibility relative to other solutions.

36 MATERIALS SCIENCE↗

TP099-CT-800DR Comparison Between SIRZ-2 and Surface Fit Method ($ρ_{e},Ζ_{e}$) Estimation

The primary objective of this test plan is to acquire dual-energy computed tomography (CT) data of 33 well-known specimens using the Leidos Reveal CT-80DR+ baggage scanner housed at LLNL (hereafter referred to as CT-80DR). These data sets will allow us to compare, for each specimen, the accuracy and precision of electron density ($ρ_{e}$) and effective atomic number ($Z_{e}$) estimates obtained from two different algorithmic methods— from TSL’s surface fits and from LLNL’s SIRZ-2 decomposition. A secondary objective is to determine how often it is necessary to acquire CT-80DR spectral response information.

42 ENGINEERING↗