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 novel technology automates the inspection process by using a scanning system that captures data within the walnut shell without having to open the shell. The system output gives a visual image inside the walnut shell sufficient to evaluate and rate the quality of the walnut.  The system uses a camara and radar that can capture data at a rapid rate. This improves speed and…

The approach is to develop a solid-state X-ray imager based on the architecture of the Silicon Drift Detector (SDD) which uses a series of cathode strips on both sides of a silicon wafer to achieve bulk depletion and electron drift.  The invention leverages this SDD functionality to achieve signal stretching of liberated charge carriers from X-Ray photons that converts the time domain…

LLNL’s novel approach is to use a continuous moving camera with a scan speed of >1 mm/sec and a frame rate of 100 frames per second.  The key is to have a light source that flashes with a duration of one nanosecond, thus essentially freezing the image with no blur.  Clear images of high resolution can then be captured through a high-magnification objective lens (reflection mode)…

LLNL’s approach to the development of a wide-field, three-dimensional quantum (3DQ) microscope is to harness quantum entangled photons to form simultaneous 3D optical images, which could be a new paradigm for 3D volumetric imaging of biological specimens.  The 3DQ microscope is comprised of a novel optical system with highly sensitive detectors and an on-demand light source of entangled…

The suppressor has a series of chambers for the propellant to flow through, but unlike all traditional suppressors, the chambers are open, not closed. The propellant is not trapped. It keeps moving. We manage its unimpeded flow through the suppressor. This is the key underlying technology of our suppressor design that enables all the improvements over the 100-year old traditional designs.

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…

Livermore Lab researchers have developed a tunable shaped charge which comprises a cylindrical liner commonly a metal such as copper or molybdenum but almost any solid material can be used and a surround layer of explosive in which the detonation front is constrained to propagate at an angle with respect to the charge axis.  The key to the concept is the ability to deposit a surrounding…

Livermore Lab researchers have developed a method that combines additive manufacturing (AM) with an infill step to render a final component which is energetic. In this case, AM is first used to print a part of the system, and this material can either be inert or energetic on its own. A second material is subsequently added to the structure via a second technique such as casting, melt…

LLNL's 3D X-ray imager combines two different hardware pieces. The first is an x-ray optic with a depth-of-field that is small compared to the object under investigation. Reflective Wolter type x-ray optics are one such design. These hollow optics have a relatively large collection efficiency and can be designed with a large field of view. The depth of focus, which is the distance over which a…

LLNL's high fidelity hydrocode is capable of predicting blast loads and directly coupling those loads to structures to predict a mechanical response. By combining this code and our expertise in modeling blast-structure interaction and damage, along with our access to experimental data and testing facilities, we can contribute to the design of protective equipment that can better mitigate the…