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.
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LLNL’s novel approach utilizes a number of techniques to improve reconstruction accuracy:
- Better coding scheme-based techniques
- Hardware-assisted techniques
- Adaptive fringe projection techniques
- Multi-exposure based techniques
The method requires specific calibration procedures and control of the hardware, which is achieved through a digital twin…
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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…
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Versatile Cold Spray (VCS) enables deposition of brittle materials, such as thermoelectrics, magnets, and insulators, while retaining their functional properties. Materials can be deposited on substrates or arbitrary shapes with no requirement to match compositions. The VCS system is low cost, easily portable, and easy to use.
VCS has been developed in a collaboration between Lawrence Livermore…