Pith Number

pith:M74WYIHS

pith:2026:M74WYIHSNO7PGLESZM53OAWY2K

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Learning to Optimize Radiotherapy Plans via Fluence Maps Diffusion Model Generation and LSTM-based Optimization

Ali Kamen, Dorin Comaniciu, Florin C. Ghesu, Isabella Poles, Marco D. Santambrogio, Martin Kraus, Riqiang Gao, Simon Arberet

A distilled diffusion model generates clinically feasible fluence maps in one shot for VMAT radiotherapy, then an LSTM refines them to meet dose goals.

arxiv:2605.13713 v1 · 2026-05-13 · cs.CV · eess.IV

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\usepackage{pith}
\pithnumber{M74WYIHSNO7PGLESZM53OAWY2K}

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

we present a diffusion-driven Learning-to-Optimize (L2O) method for end-to-end VMAT planning. A distribution-matching distilled diffusion model learns a clinically feasible manifold of fluence maps, enabling their one-shot generation. On top of this, an LSTM-based L2O module learns gradient update dynamics to swiftly refine fluence maps toward prescribed dose objectives during inference.

C2weakest assumption

The learned fluence map manifold is clinically feasible and the LSTM module can learn stable gradient update dynamics that generalize to new patient geometries without post-hoc tuning or safety overrides.

C3one line summary

A distilled diffusion model generates clinically feasible fluence maps for VMAT and an LSTM-based optimizer refines them to meet dose objectives, improving efficiency and deliverability on prostate cancer data.

References

33 extracted · 33 resolved · 2 Pith anchors

[1] arXiv preprint arXiv:2603.06338 (2026) 2026

[2] Medical Physics52(5), 3183–3190 (2025) 2025

[3] In: Seminars in radiation oncology 2022

[4] arXiv preprint arXiv:2305.18014 (2023) 2023

[5] Physics in Medicine & Biology68(15), 155006 (2023) 2023

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T02:44:16.731048Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

67f96c20f26bbef32c92cb3bb702d8d2b1accca6984b795eb6b7050a365e1e87

Aliases

arxiv: 2605.13713 · arxiv_version: 2605.13713v1 · doi: 10.48550/arxiv.2605.13713 · pith_short_12: M74WYIHSNO7P · pith_short_16: M74WYIHSNO7PGLES · pith_short_8: M74WYIHS

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/M74WYIHSNO7PGLESZM53OAWY2K \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 67f96c20f26bbef32c92cb3bb702d8d2b1accca6984b795eb6b7050a365e1e87

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "750d9fa0fcadb2fc0f5984f4b4e027f243e819512ac3e2c961ca43fe439043e4",
    "cross_cats_sorted": [
      "eess.IV"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T16:00:11Z",
    "title_canon_sha256": "b3165fe769a161958e642e41b266570ce11a2794c3c077bc2e0add645cb5dc8a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13713",
    "kind": "arxiv",
    "version": 1
  }
}