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. 

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’s high throughput method involves proteome-wide screening for linear B-cell epitopes using native proteomes isolated from a pathogen of interest and convalescent sera from immunized animals.

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 has a patented process to produce colloidal silica directly from geothermal fluids. Livermore’s process uses membranes to produce a mono-dispense slurry of colloidal silica particles for which there are several applications. LLNL has demonstrated that colloidal silica solutions that result from extraction of silica from geothermal fluids undergo a transition to a solid gel over a range of…

LLNL has developed a novel process of production, isolation, characterization, and functional re-constitution of membrane-associated proteins in a single step. In addition, LLNL has developed a colorimetric assay that indicates production, correct folding, and incorporation of bR into soluble nanolipoprotein particles (NLPs).

LLNL has developed an approach, for formation of NLP/…