pith:33NIG5ZU
A Fast and Generic Energy-Shifting Transformer for Hybrid Monte Carlo Radiotherapy Calculation
A deep learning method called Energy-Shifting generates clinical 6 MV LINAC dose maps from monoenergetic inputs and passes 98 percent gamma criteria in prostate radiotherapy planning.
arxiv:2604.09157 v2 · 2026-04-10 · physics.med-ph · cs.LG
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Claims
Our proposed pipeline achieves a Gamma Passing Rate exceeding 98% (3%/3mm) compared to the MC reference, evaluated within the framework of a treatment planning system (TPS) for prostate radiotherapy.
That synthesizing dose from monoenergetic inputs under identical beam configurations preserves beam profile integrity and generalizes to unseen clinical datasets without introducing clinically relevant errors in heterogeneous anatomy.
A hybrid Transformer-UNet model with energy-shifting inputs generates 6 MV LINAC dose maps from monoenergetic data, achieving over 98% gamma passing rate (3%/3mm) versus full Monte Carlo for prostate radiotherapy.
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| First computed | 2026-05-27T01:05:54.116842Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
deda837734bebb93ea4238095849d0d231fd03f1fe05defb1ce0cb083791fced
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/33NIG5ZUX25ZH2SCHAEVQSOQ2I \
| jq -c '.canonical_record' \
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Canonical record JSON
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