ArticleAnnals of nuclear medicine2026
Deep learning-guided attenuation and scatter correction of
Article in Annals of nuclear medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Artificial Intelligence Across the Cancer Theranostics Workflow: Critical Appraisal of Current Evidence and Future Clinical Translation.Molecular imaging and biology · 2026Review
- Towards practical radiopharmaceutical treatment planning: a review of dosimetry simplification techniques.Theranostics · 2026Review
- Artificial intelligence across oncologic theranostics: evidence for patient stratification, dosimetry, and adaptive radiopharmaceutical therapy.Frontiers in nuclear medicine · 2026Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to develop deep learning (DL) models for CT-free attenuation correction and Monte Carlo-based scatter correction in MATERIALS AND
methodsData from 222 patients who underwent
resultsThe average (± SD) of the voxel-wise mean error (ME) was ≤ 0.003 Gy for all tasks. The Relative Error (RE (%)) for AC, SC, and ASC tasks were 4.64 ± 7.52%, 8.99 ± 26.35%, and 16.45 ± 25.83%, respectively. Voxel-level Gamma evaluations within the whole body using three different criteria sets, including "DTA: 4.79 mm, DD: 1%"; "DTA: 10 mm, DD: 5%"; and "DTA: 15 mm, DD: 10%" yielded pass rates of over 99.60%. The mean absolute error (MAE) for lesions, normal liver and lungs across all tasks were 3.16 ± 3.39, 0.35 ± 0.36, 0.41 ± 0.47 Gy for AC, 1.97 ± 2.79, 0.19 ± 0.16, 0.22 ± 0.20 Gy, for SC and 5.16 ± 7.10, 0.45 ± 0.51, and 0.34 ± 0.37 Gy for ASC, respectively.
conclusionMultiple models were developed for key SPECT quantification tasks, with potential value in clinical setting lacking reliable CT data or sufficient computational resources for Monte Carlo simulations. The models look promising for potential clinical translation and integration into commercial reconstruction software.
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Registered trials
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