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.
This invention configures multiple spherical substrate targets to roll independently of one another. The spheres’ rolling motion is deliberately randomized to promote uniform coating while eliminating the interaction (rubbing, sliding) of adjacent spheres that is present in conventional sphere coating designs. The devices’ novel structure features enable the collimation of depositing…
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…
There are prominent technical challenges arising from spinning a battery on the order of kilohertz as required by magic angle spinning in order to obtain spectral resolution that are addressed and enable operando solid-state NMR. The operando NMR measurement allows for continuous monitoring of the battery components and of potential metastable states that may exist during…