{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11508101","patent":{"patent_number":"US-11508101","title":"Dynamic dual-tracer PET reconstruction method based on hybrid-loss 3D convolutional neural networks","assignee":null,"inventors":[],"filing_date":"2019-03-27T00:00:00.000Z","publication_date":"2022-11-22T00:00:00.000Z","cpc_codes":["G06T","A61B","A61B","G06N","G06N","G06N","G06T","A61B","G06N","G06T","G06T"],"num_claims":8,"abstract":"This present invention discloses a dynamic dual-tracer PET reconstruction method based on a hybrid-loss 3D CNN, which selects a corresponding 3D convolution kernel for a 3D format of dual-tracer PET data, and performs feature extraction in a stereoscopic receptive field (down-sampling) and the reconstruction (up-sampling) process, which accurately reconstructs the three-dimensional concentration distributions of two different tracers from the dynamic sinogram. The method of the invention can better reconstruct the simultaneous-injection single-acquisition dual-tracer sinogram without any model constraints. The scanning time required for dual-tracer PET can be minimized based on the method of the present invention.Using this method, the raw sinogram data of dual tracers can be reconstructed into two volumetric individual images in a short time."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Dynamic dual-tracer PET reconstruction method based on hybrid-loss 3D convolutional neural networks","description":"This present invention discloses a dynamic dual-tracer PET reconstruction method based on a hybrid-loss 3D CNN, which selects a corresponding 3D convolution kernel for a 3D format of dual-tracer PET d","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11508101","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-11508101","citation_suggestion":"Patentable. \"Dynamic dual-tracer PET reconstruction method based on hybrid-loss 3D convolutional neural networks\" (US-11508101). https://patentable.app/patents/US-11508101","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11508101","json":"https://patentable.app/api/llm-context/US-11508101","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T13:52:02.699Z"}