Millisecond-Level VMAT Plan Dose Calculation for Cervical Cancer With a Mamba-Based UNet Model

This study aims to develop a mamba-based UNet model (MambaUNet) for fast and accurate volumetric modulated arc therapy (VMAT) plan dose calculation in cervical cancer. The 2D fluence maps from all control points were projected into a 3D fluence volume and combined with the corresponding density volume as input to MambaUNet, which subsequently calculated the 3D dose distribution. The model integrated volumetric feature extraction via a 3D residual UNet and efficient global dependency modeling through a mamba encoder. A total of 633 cervical cancer VMAT plans were used, with 445 allocated for training, 128 for validation, and 60 for testing. The MambaUNet model was also comparatively evaluated against three other models (iDoTA, TransUNet and ResUNet) in terms of dose calculation accuracy and computational efficiency. On the test set, the dose-volume histograms (DVHs), mean absolute errors (MAEs), root mean square errors (RMSEs), Dice similarity coefficients (DSCs), and dosimetric indices between the model-calculated doses and the ground truth doses all demonstrated good agreement. The proposed MambaUNet model achieved an average 3D gamma passing rate of 98.79 ± 0.78% under the 3%/2 mm criterion. It provided dose calculation accuracy comparable to or slightly better than the other models and achieved higher computational efficiency.The entire dose calculation process, including fluence map generation from MLC sequences, fluence projection and MambaUNet model inference, can be completed within 80 milliseconds on average. These results suggest that MambaUNet has strong potential for accelerating the VMAT planning process.