Optimizing Attenuation Correction in 68Ga-PSMA PET Imaging Using Deep Learning and Artifact-Free Dataset Refinement

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dc.contributor.authorGiv, Masoumeh Dorri
dc.contributor.authorOzbolat, Guluzar
dc.contributor.authorArabi, Hossein
dc.contributor.authorMalmir, Somayeh
dc.contributor.authorNaseri, Shahrokh
dc.contributor.authorRavan, Vahid Roshan
dc.contributor.authorAkbari-Lalimi, Hossein
dc.date.accessioned2026-04-25T14:20:27Z
dc.date.available2026-04-25T14:20:27Z
dc.date.issued2025
dc.departmentSinop Üniversitesi
dc.description.abstractBackground/Objectives: Attenuation correction (AC) is essential for achieving quantitatively accurate PET imaging. In 68Ga-PSMA PET, however, artifacts such as respiratory motion, halo effects, and truncation errors in CT-based AC (CT-AC) images compromise image quality and impair model training for deep learning-based AC. This study proposes a novel artifact-refinement framework that filters out corrupted PET-CT images to create a clean dataset for training an image-domain AC model, eliminating the need for anatomical reference scans. Methods: A residual neural network (ResNet) was trained using paired PET non-AC and PET CT-AC images from a dataset of 828 whole-body 68Ga-PSMA PET-CT scans. An initial model was trained using all data and employed to identify artifact-affected samples via voxel-level error metrics. These outliers were excluded, and the refined dataset was used to retrain the model with an L2 loss function. Performance was evaluated using metrics including mean error (ME), mean absolute error (MAE), relative error (RE%), RMSE, and SSIM on both internal and external test datasets. Results: The model trained with the artifact-free dataset demonstrated significantly improved performance: ME = -0.009 +/- 0.43 SUV, MAE = 0.09 +/- 0.41 SUV, and SSIM = 0.96 +/- 0.03. Compared to the model trained on unfiltered data, the purified data model showed enhanced quantitative accuracy and robustness in external validation. Conclusions: The proposed data purification framework significantly enhances the performance of deep learning-based AC for 68Ga-PSMA PET by mitigating artifact-induced errors. This approach facilitates reliable PET imaging in the absence of anatomical references, advancing clinical applicability and image fidelity.
dc.description.sponsorshipSwiss National Science Foundation [SNSF 320030_176052 and SNSF 320030_173091] Funding Source: Medline
dc.identifier.doi10.3390/diagnostics15111400
dc.identifier.issn2075-4418
dc.identifier.issue11
dc.identifier.orcid0000-0001-6526-0960
dc.identifier.orcid0000-0003-4149-9250
dc.identifier.orcid0000-0002-2973-2251
dc.identifier.orcid0000-0001-6196-6982
dc.identifier.pmid40506972
dc.identifier.scopus2-s2.0-105008507727
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics15111400
dc.identifier.urihttps://hdl.handle.net/11486/8587
dc.identifier.volume15
dc.identifier.wosWOS:001505920000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260420
dc.subjectattenuation correction
dc.subjectpositron emission tomography computed tomography
dc.subjectdeep learning
dc.subjectimage artifacts
dc.subjectneural networks
dc.titleOptimizing Attenuation Correction in 68Ga-PSMA PET Imaging Using Deep Learning and Artifact-Free Dataset Refinement
dc.typeArticle

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