Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy
Abstract
PURPOSE: Intensity-modulated proton therapy (IMPT) is an advanced treatment modality for head and neck (H&N) cancer patients, offering precise tumor dose coverage while sparing surrounding organs at risk (OARs). However, IMPT is highly sensitive to inter-fractional anatomical changes, necessitating periodic adjustments through online adaptive radiation therapy (oART). But a significant bottleneck in the current oART workflow is the need for fast and accurate dose calculation using Monte Carlo (MC) simulations for plan quality assessment and re-optimization. Reducing the number of particles in MC-based simulations can accelerate dose calculation but at the cost of reduced accuracy. To address this, denoising noisy dose maps generated by low statistics MC simulations has been proposed as a method to rapidly and accurately generate high-accuracy dose maps.
METHODS: A diffusion transformer-based dose denoising framework was developed. IMPT treatment plans and 3D CT images from 80 H&N cancer patients were used to construct the training dataset by generating noisy dose maps and their corresponding high statistics dose maps using an open-source MC software, MCsquare, with a computation time of approximately 1 minutes and 10 minutes per plan, respectively. Each data sample was standardized into uniform chunks with zero-padding. Then, normalization and non-linear mapping were applied to the data samples to transform them toward a quasi-Gaussian distribution. The treatment plans and…
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