Accelerated Dose Generation in Gamma Knife Radiosurgery Using a Wavelet Diffusion Model for Sparse Representation

Accurate dose calculation is critical in Gamma Knife Radiosurgery (GKRS), especially in regions near the skull where tissue heterogeneity can significantly alter dose distributions. Although the convolution algorithm-based dose calculation (Conv dose) using CT enables heterogeneity correction for an accurate treatment plan, it introduces additional clinical burdens, including longer planning times and increased radiation exposure. This study is the first to explore using the conditional wavelet Denoising Diffusion Probabilistic Model (cwDDPM) to generate radiation dose distributions. cwDDPM exploits the inherent sparsity of radiation dose maps in the wavelet domain to achieve more efficient learning and sampling. As a result, it produces synthetic convolution-based (sConv) doses quickly and accurately, without relying on CT imaging or computationally intensive convolution calculations. Quantitative results across isodose overlap and dose-volume metrics demonstrate that cwDDPM achieves high fidelity to the ground truth Conv dose and performs comparably to a state-of-the-art diffusion model while reducing inference time by up to 45-fold. cwDDPM also demonstrates robustness across different tumor locations, especially near the skull, where heterogeneity correction is most critical. These results suggest that cwDDPM is a promising solution for rapid, CT-free Conv dose generation in GKRS planning.