{"paper":{"title":"Joint Reconstruction of Activity and Attenuation in PET by Diffusion Posterior Sampling in Wavelet Coefficient Space","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Joint PET activity and attenuation maps can be reconstructed from emission data alone via diffusion posterior sampling in wavelet space.","cross_cats":[],"primary_cat":"physics.med-ph","authors_text":"Alexandre Bousse, Antoine De Paepe, Baptiste Laurent, Catherine Cheze-Le-Rest, Cl\\'ementine Phung-Ngoc, Dimitris Visvikis, Hong-Phuong Dang, Olivier Saut, Thibaut Merlin","submitted_at":"2025-05-24T16:39:50Z","abstract_excerpt":"Attenuation correction (AC) is necessary for accurate activity quantification in positron emission tomography (PET). Conventional reconstruction methods typically rely on attenuation maps derived from a co-registered computed tomography (CT) or magnetic resonance (MR) scan. However, this additional scan may complicate the imaging workflow, introduce misalignment artifacts and increase radiation exposure. In this paper, we propose a joint reconstruction of activity and attenuation (JRAA) approach that eliminates the need for auxiliary anatomical imaging by relying solely on emission data. This "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experimental results on simulated data show our method outperforms maximum likelihood activity and attenuation (MLAA) and MLAA-UNet with U-Net-based post processing, and yields high-quality noise-free reconstructions across various count settings with time-of-flight (TOF).","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The pre-trained wavelet diffusion model generalizes to unseen patient anatomies, scanner geometries, and count levels without significant domain shift or retraining.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A wavelet diffusion model combined with diffusion posterior sampling enables joint 3D activity-attenuation reconstruction in PET from emission data alone, outperforming MLAA on simulated TOF data.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Joint PET activity and attenuation maps can be reconstructed from emission data alone via diffusion posterior sampling in wavelet space.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"20e1d9b9c8481a30ac69b55d6308e94a7e3fc3cac4a86e6ad9849a40fd87c9d6"},"source":{"id":"2505.18782","kind":"arxiv","version":7},"verdict":{"id":"96d81d73-3ca5-4225-8bf1-c327f006e9d4","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T14:14:38.077877Z","strongest_claim":"Experimental results on simulated data show our method outperforms maximum likelihood activity and attenuation (MLAA) and MLAA-UNet with U-Net-based post processing, and yields high-quality noise-free reconstructions across various count settings with time-of-flight (TOF).","one_line_summary":"A wavelet diffusion model combined with diffusion posterior sampling enables joint 3D activity-attenuation reconstruction in PET from emission data alone, outperforming MLAA on simulated TOF data.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The pre-trained wavelet diffusion model generalizes to unseen patient anatomies, scanner geometries, and count levels without significant domain shift or retraining.","pith_extraction_headline":"Joint PET activity and attenuation maps can be reconstructed from emission data alone via diffusion posterior sampling in wavelet space."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.18782/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"7e45ee682b2cf478458a206c7a88c493da2448066af759544b8e1fd06da45144"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}