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SUMMARY:A Fast and Generic Energy‑Shifting Transformer for Hybrid Monte 
 Carlo Radiotherapy Dose Calculation
DTSTART;VALUE=DATE-TIME:20260909T091000Z
DTEND;VALUE=DATE-TIME:20260909T094000Z
DTSTAMP;VALUE=DATE-TIME:20260922T182702Z
UID:indico-contribution-2021@events.ncbj.gov.pl
DESCRIPTION:Speakers: Chi-Hieu Pham (LaTIM)\, Julien Bert (LaTIM\, UMR1101
  INSERM)\nWe introduce a novel learning framework for accelerated Monte Ca
 rlo (MC) dose calculation termed Energy Shifting. This approach leverages 
 deep learning to synthesize complex polyenergetic dose distributions direc
 tly from simple monoenergetic inputs under identical beam configurations. 
 Unlike conventional approaches such as denoising or GenAI techniques\, our
  method achieves superior cross-domain generalization on unseen datasets b
 y integrating high-fidelity anatomical textures and source-specific beam s
 imilarity directly into the model’s input space. Furthermore\, we propos
 e a novel 3D architecture termed TransUNetSE3D\, featuring Transformer blo
 cks for global context and Residual Squeeze-and-Excitation modules for ada
 ptive channel-wise feature recalibration. Hierarchical representations of 
 these blocks are fused into the network’s latent space alongside the pri
 mary dose-map parameters\, allowing physics-aware reconstruction. This hyb
 rid design outperforms existing U-Net and Transformer-based benchmarks in 
 both spatial precision and structural preservation\, while maintaining the
  execution speed necessary for real-time use. Our proposed pipeline achiev
 es a Gamma Passing Rate exceeding 98% (3%/3mm) compared to the MC referenc
 e\, evaluated within the framework of a treatment planning system using 6M
 V TrueBeam Linear Accelerator for prostate radiotherapy. These results off
 er a robust solution for fast volumetric dosimetry in adaptive radiotherap
 y.\n\nhttps://events.ncbj.gov.pl/event/468/contributions/2021/
LOCATION:
URL:https://events.ncbj.gov.pl/event/468/contributions/2021/
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