{"canonical":"https://abierto.us/opportunities/il13625","key":"IL13625","url":"https://abierto.us/opportunities/il13625","title":"TECHNOLOGY/BUSINESS OPPORTUNITY Dynamic 4DCT Reconstruction using Neural Representation-based Optimization","solicitation_number":"IL-13625","notice_type":"s","open":false,"response_deadline":"2024-04-12T16:00:00Z","first_posted":"2024-03-12","last_posted":"2024-03-12","department":"ENERGY, DEPARTMENT OF","subagency":"ENERGY, DEPARTMENT OF","office":"LLNS – DOE CONTRACTOR","naics":"334517","psc":null,"set_aside":null,"place_state":"CA","place_county":"06001","place_county_name":"Alameda County","place_city":"0641992","place_city_name":"Livermore","winner":null,"award_amount":null,"publications":[{"notice_id":"4564725fb3e044d8851a33c6e16ab189","title":"TECHNOLOGY/BUSINESS OPPORTUNITY","solicitation_number":"IL-13625","notice_type":"s","base_type":"s","posted":"2024-03-12","posted_at":null,"due_at":"2024-04-12T16:00:00Z","due_date":"2024-04-12","cancelled":null,"archived":null,"archive_date":"2024-04-27","award_number":null,"awardee_name":null,"amount":null,"link_sam":"https://sam.gov/workspace/contract/opp/4564725fb3e044d8851a33c6e16ab189/view","enriched":false,"history":[]},{"notice_id":"dc10559641fd44e991974487ee5a0b04","title":"TECHNOLOGY/BUSINESS OPPORTUNITY Dynamic 4DCT Reconstruction using Neural Representation-based Optimization","solicitation_number":"IL-13625","notice_type":"s","base_type":"s","posted":"2024-03-12","posted_at":null,"due_at":"2024-04-12T16:00:00Z","due_date":"2024-04-12","cancelled":null,"archived":null,"archive_date":"2024-04-27","award_number":null,"awardee_name":null,"amount":null,"link_sam":"https://sam.gov/workspace/contract/opp/dc10559641fd44e991974487ee5a0b04/view","enriched":false,"history":[]}],"latest_notice_id":"dc10559641fd44e991974487ee5a0b04","first_type":"s","notices":[{"dates":{"posted":"2024-03-12","response_deadline":{"raw":"2024-04-12T09:00:00-07:00","utc":"2024-04-12T16:00:00Z","date":"2024-04-12","time":"09:00:00","utc_offset_seconds":-25200}},"links":{"sam":"https://sam.gov/workspace/contract/opp/4564725fb3e044d8851a33c6e16ab189/view"},"naics":{"codes":["334517"],"primary":"334517"},"title":"TECHNOLOGY/BUSINESS OPPORTUNITY","agency":{"office":{"code":"899011","name":"LLNS – DOE CONTRACTOR"},"subtier":{"code":"8900","name":"ENERGY, DEPARTMENT OF"},"department":{"code":"089","name":"ENERGY, DEPARTMENT OF"},"office_address":{"zip":"94551","city":"Livermore","state":"CA","country":"USA"},"organization_type":"OFFICE"},"status":{"active":false,"archive_date":"2024-04-27","archive_type":"auto15"},"contacts":[{"name":"Mary Holden-Sanchez","role":"primary","email":"holdensanchez2@llnl.gov","phone":"9254224614"},{"name":"Charlotte Eng","role":"secondary","email":"eng23@llnl.gov","phone":"9254221905"}],"base_type":{"code":"s","label":"Special Notice"},"notice_id":"4564725fb3e044d8851a33c6e16ab189","provenance":{"extract":{"url":"https://s3.amazonaws.com/falextracts/Contract%20Opportunities/Archived%20Data/FY2024_archived_opportunities.csv","etag":"\"d582488fe153a9f11bf629913d176ffc-137\"","fetched_at":"2026-09-16T19:07:39.720164Z","row_sha256":"834da466873ece8352c1862e095ee41d6908bb9d8ac47b04e29dbc827cc7045b","last_modified":"2026-09-13T14:47:40Z"},"updated_at":"2026-09-16T19:07:39.720164Z","first_seen_at":"2026-09-16T19:07:39.720164Z"},"description":{"text":"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. 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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. 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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. 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