ArticleScientific reports2019
Deep-dose: a voxel dose estimation method using deep convolutional neural network for personalized internal dosimetry.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
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- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Swin UNETR-based prediction of [BMC medical imaging · 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
- Deep-Learning-Based 3D Dose Distribution Prediction for VMAT Lung Cancer Treatment Using an Enhanced UNet3D Architecture with Composite Loss Functions.Bioengineering (Basel, Switzerland) · 2026Article
- Towards practical radiopharmaceutical treatment planning: a review of dosimetry simplification techniques.Theranostics · 2026Review
- The evolution of surrogate microspheres in transarterial radioembolization: from SPECT prediction to multimodal AI-guided precision dosimetry.Theranostics · 2026Review
- Dose Prediction Deep Learning-Based Model for VMAT of Prostate Cancer Applying Magnetic Resonance Image (MRI) in Versa HD Linear Accelerator.Advanced biomedical research · 2026Article
- The Role of Artificial Intelligence in Theranostics.Journal of nuclear medicine technology · 2025Review
- The Role of Artificial Intelligence in Advancing Theranostics Dosimetry for Cancer Therapy: a Review.Nuclear medicine and molecular imaging · 2025Review
- Optimizing dosimetry in Y-90 microsphere radioembolization: GPU-accelerated Monte Carlo simulation versus conventional methods for high-volume setting.EJNMMI physics · 2025Article
- AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view.Scientific reports · 2025Article
- Deep learning-based prediction of Monte Carlo dose distribution for heavy ion therapy.Physics and imaging in radiation oncology · 2025Article
- Deep learning-based organ-wise dosimetry ofScientific reports · 2025Article
- Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy.Diagnostics (Basel, Switzerland) · 2025Review
- Targeted radioligand therapy: physics and biology, internal dosimetry and other practical aspects duringTheranostics · 2025Review
- Monte Carlo methods for medical imaging research.Biomedical engineering letters · 2024Review
- A comprehensive review on Compton camera image reconstruction: from principles to AI innovations.Biomedical engineering letters · 2024Review
- Strategies for mitigating inter-crystal scattering effects in positron emission tomography: a comprehensive review.Biomedical engineering letters · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Personalized dosimetry with high accuracy is crucial owing to the growing interests in personalized medicine. The direct Monte Carlo simulation is considered as a state-of-art voxel-based dosimetry technique; however, it incurs an excessive computational cost and time. To overcome the limitations of the direct Monte Carlo approach, we propose using a deep convolutional neural network (CNN) for the voxel dose prediction. PET and CT image patches were used as inputs for the CNN with the given ground truth from direct Monte Carlo. The predicted voxel dose rate maps from the CNN were compared with the ground truth and dose rate maps generated voxel S-value (VSV) kernel convolution method, which is one of the common voxel-based dosimetry techniques. The CNN-based dose rate map agreed well with the ground truth with voxel dose rate errors of 2.54% ± 2.09%. The VSV kernel approach showed a voxel error of 9.97% ± 1.79%. In the whole-body dosimetry study, the average organ absorbed dose errors were 1.07%, 9.43%, and 34.22% for the CNN, VSV, and OLINDA/EXM dosimetry software, respectively. The proposed CNN-based dosimetry method showed improvements compared to the conventional dosimetry approaches and showed results comparable with that of the direct Monte Carlo simulation with significantly lower calculation time.
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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.