LLNL’s Distributed Implicit Neural Representation (DINR) is a novel approach to 4D time-space reconstruction of dynamic objects. DINR is the first technology to enable 4D imaging of dynamic objects at sufficiently high spatial and temporal resolutions that are necessary for real world medical and industrial applications.
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The essence of this invention is a method that couples network architecture using neural implicit representations coupled with a novel parametric motion field to perform limited angle 4D-CT reconstruction of deforming scenes.
LLNL researchers have developed a technology suite that includes several methods for detecting trace levels of illicit drugs even in mixtures. These methods can be used as a rapid screening test for incoming samples; for the samples that were determined to contain detectable amounts, they would undergo final verification using conventional laboratory analytical techniques.
To solve these challenges using new and existing CT system designs, LLNL has developed an innovative software package for CT data processing and reconstruction. Livermore Tomography Tools (LTT) is a modern integrated software package that includes all aspects of CT modeling, simulation, reconstruction, and analysis algorithms based on the latest research in the field. LTT contains the most…
LLNL researchers have developed a custom resin formulation which uses a dispersing solvent and only a multifunctional monomer as the binding agent. The dispersing solvent system typically used has multiple components meant to achieve excellent dispersal of silica in order to create a flowable resin (rather than a paste). The dispersing agent has low vapor pressure, which allows the 3D printed…