ArticleNuclear medicine and molecular imaging2023
Voxel-Based Internal Dosimetry for
Article in Nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 14 citations in OpenAlex.
- Outcome-driven dosimetry optimization for [European journal of nuclear medicine and molecular imaging · 2026Article
- Comparative Dosimetry of Single and HybridDiseases (Basel, Switzerland) · 2026Article
- Personalised Dosimetry in Nuclear Medicine: Bridging Physics, Biology and AI for Next Generation Radiopharmaceutical Therapy.Nuclear medicine and molecular imaging · 2026Review
- Design and validation of a deep learning GAN for predictingJournal of applied clinical medical physics · 2026Article
- The evolution of surrogate microspheres in transarterial radioembolization: from SPECT prediction to multimodal AI-guided precision dosimetry.Theranostics · 2026Review
- The Role of Artificial Intelligence in Theranostics.Journal of nuclear medicine technology · 2025Review
- AI in SPECT Imaging: Opportunities and Challenges.Seminars in nuclear medicine · 2025Review
- Monte Carlo methods for medical imaging research.Biomedical engineering letters · 2024Review
- Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial Normalization.Nuclear medicine and molecular imaging · 2024Article
- An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 3: Contribution from Calculating Absorbed Dose from Time-Integrated Activity.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [European journal of nuclear medicine and molecular imaging · 2024Article
- Theranostics and artificial intelligence: new frontiers in personalized medicine.Theranostics · 2024Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: In this study, we propose a deep learning (DL)-based voxel-based dosimetry method in which dose maps acquired using the multiple voxel S-value (VSV) approach were used for residual learning. Methods: Twenty-two SPECT/CT datasets from seven patients who underwent Results: The DL approach provided a slightly more accurate estimation than the multiple-VSV approach, but the results were not statistically significant. The single-VSV approach yielded a relatively inaccurate estimation. No significant difference was noted between the multiple VSV and DL approach on the dose maps. However, this difference was prominent in the error maps. The multiple VSV and DL approach showed a similar correlation. In contrast, the multiple VSV approach underestimated doses in the low-dose range, but it accounted for the underestimation when the DL approach was applied. Conclusion: Dose estimation using the deep learning-based approach was approximately equal to that in the MC simulation. Accordingly, the proposed deep learning network is useful for accurate and fast dosimetry after radiation therapy using
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.