# TECHNOLOGY/BUSINESS OPPORTUNITY Dynamic 4DCT Reconstruction using Neural Representation-based Optimization Canonical: https://abierto.us/opportunities/il13625 - Solicitation number: IL-13625 - Notice type: Special notice - Status: Closed. Deadline was April 12, 2024 at 12:00 PM EDT - Department: Department of Energy - Contracting office: LLNS – DOE Contractor (899011) - NAICS: 334517 Irradiation Apparatus Manufacturing - Place of performance: Livermore, California - County: Alameda County (FIPS 06001). https://abierto.us/counties/alameda-county-ca-06001 - City: Livermore. https://abierto.us/cities/livermore-ca-0641992 - First posted: March 12, 2024 - Last posted: March 12, 2024 - SAM.gov: https://sam.gov/workspace/contract/opp/dc10559641fd44e991974487ee5a0b04/view ## Description **Opportunity:** Lawrence Livermore National Laboratory (LLNL), operated by the Lawrence Livermore National Security (LLNS), LLC under contract no. DE-AC52-07NA27344 (Contract 44) with the U.S. Department of Energy (DOE), is offering the opportunity to enter into a collaboration to further develop and commercialize its Dynamic 4DCT Reconstruction using Neural Representation-based Optimization. **Background:** Reconstructing moving scenes with computed tomography (4DCT) is a challenging and ill-posed problem with important applications in industrial and medical settings. Dynamic computed tomography (DCT) refers to image reconstruction of moving or non-rigid objects over time while x-ray projections are acquired over a range of angles. Although 4DCT reconstruction is widely applicable to the study of object deformation and dynamics in a number of industrial and clinical applications, it has been a long-standing challenge due to the complexity of the x-ray measurement capturing both spatial and temporal features with the limited data sampling. **Description:** 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. To enable the reconstruction of the scene with high dynamics, the inventors developed a novel method for dynamic 4DCT reconstruction that leverages implicit neural representations with a parametric motion field to reconstruct dynamic scenes as time-varying sequence of 3D volumes. The methods have been demonstrated in experiments that reconstruct dynamic scenes with deformable and periodic motion on physically simulated synthetic data and real data. **Advantages/Benefits:** The principal advantages of this invention are: This method is an end-to-end optimization approach without the need for any training data; This method eliminates the need for fast CT scanners in use cases where the object or scene being scanned is fast moving; The hierarchical coarse-to-fine procedure to estimate the motion field enables recovering fine details of the motion scene without suffering from severe artifacts due to poor convergence of the optimization. **Potential Applications:** CT/CAT (computerized axial tomography) scanner systems Development Status: Current stage of technology development: TR-2 LLNL has patent(s) on this invention. U.S. Patent No. 11,741,643 Reconstruction of dynamic scenes based on differences between collected view and synthesized view published 8/29/2023 LLNL is seeking industry partners with a demonstrated ability to bring such inventions to the market. Moving critical technology beyond the Laboratory to the commercial world helps our licensees gain a competitive edge in the marketplace. All licensing activities are conducted under policies relating to the strict nondisclosure of company proprietary information. Please visit the IPO website at https://ipo.llnl.gov/resources for more information on working with LLNL and the industrial partnering and technology transfer process. **Note:** THIS IS NOT A PROCUREMENT. Companies interested in commercializing LLNL's Dynamic 4DCT Reconstruction using Neural Representation-based Optimization should provide an electronic OR written statement of interest, which includes the following: Company Name and address. The name, address, and telephone number of a point of contact. A description of corporate expertise and/or facilities relevant to commercializing this technology. Please provide a complete electronic OR written statement to ensure consideration of your interest in LLNL's Dynamic 4DCT Reconstruction using Neural Representation-based Optimization. The subject heading in an email response should include the Notice ID and/or the title of LLNL’s Technology/Business Opportunity and directed to the Primary and Secondary Point of Contacts listed below. Written responses should be directed to: Lawrence Livermore National Laboratory Innovation and Partnerships Office P.O. **Box 808, L-779 Livermore, CA 94551-0808 Attention:** IL-13625 ## Publications - March 12, 2024: Special notice, due April 12, 2024 at 12:00 PM EDT. Notice 4564725fb3e044d8851a33c6e16ab189. https://sam.gov/workspace/contract/opp/4564725fb3e044d8851a33c6e16ab189/view - March 12, 2024: Special notice, due April 12, 2024 at 12:00 PM EDT. Notice dc10559641fd44e991974487ee5a0b04. https://sam.gov/workspace/contract/opp/dc10559641fd44e991974487ee5a0b04/view ## Points of contact - Mary Holden-Sanchez, holdensanchez2@llnl.gov, 9254224614 - Charlotte Eng, eng23@llnl.gov, 9254221905 --- Source: SAM.gov Contract Opportunities bulk extract. Confirm deadlines on SAM.gov before responding. Cite https://abierto.us/opportunities/il13625.