UNetrDose: a fast and accurate transformer-based dose prediction model for radiotherapy.
Authors
Affiliations (4)
Affiliations (4)
- College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, Sichuan, 610065, China.
- Department of Radiotherapy Physics & Technology, Sichuan University West China Hospital, No. 37, Guoxue Alley, Wuhou District, Chengdu, Sichuan, 610041, China.
- Radiotherapy Physics & Technology Center, Cancer Center, Sichuan University West China Hospital, 37 Guoxue Lane, Wuhou District, Chengdu, Sichuan, 610041, China.
- College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, 610065, China.
Abstract
To develop and validate UNetrDose, a Transformer-based deep learning model designed for fast and accurate photon beamlet dose prediction. The study aims to achieve Monte Carlo (MC)-level dosimetric accuracy using only beamlet-specific CT images and beamlet coordinates as input, enabling the efficient reconstruction of complete 3D dose distributions for intensity-modulated radiation therapy (IMRT) plans. 
Approach. For each beamlet, a fixed-size 3D CT patch was extracted along its propagation path, centered on the beamlet trajectory. The geometric information, defined as the beamlet's relative position within the beam field, was used alongside the CT patch as model input. The ground-truth dose distributions were generated using MC simulations. The proposed UNetrDose model combined convolutional layers for local feature extraction with Transformer modules to capture long-range dependencies. A total of 90 fixed-beam IMRT plans (51 esophageal and 39 rectal cases) were used for model training and validation. Model performance was comprehensively assessed, evaluating spatial accuracy with 3D gamma pass rates and clinical acceptability through Dose-Volume Histogram (DVH) comparisons and other dosimetric parameters. 
Main results. UNetrDose demonstrated high fundamental accuracy at the individual beamlet level, where pass rates for the stringent γ(1 mm, 1%) criterion exceeded 96% for both esophageal and rectal cases. This translated to excellent clinical performance on full IMRT plans, where the model achieved mean γ(2 mm, 2%) pass rates ranging from 97.06 ± 2.05% for esophageal cases to 98.75 ± 0.78% for rectal cases. The model was also highly efficient, with an average inference time of approximately 28 ms per beamlet. 
Significance. UNetrDose offers a promising alternative to traditional dose calculation engines by providing a balance between high dosimetric accuracy and fast computation. Its ability to predict dose distributions using only CT images and beamlet positions simplifies the workflow, making it highly applicable for time-sensitive clinical scenarios. 
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