pith:UFZCXLZH
Generating synthetic computed tomography for radiotherapy: SynthRAD2025 challenge report
Deep learning generates clinically usable synthetic CT from CBCT for radiotherapy dose planning, but MRI conversion remains challenging.
arxiv:2605.13555 v1 · 2026-05-13 · physics.med-ph · cs.AI
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Claims
SynthRAD2025 demonstrates that deep learning yields clinically relevant sCTs, especially for CBCT-to-CT, while identifying persistent MRI-to-CT challenges and underscoring dose-based evaluation as essential for clinical validation.
That the reported top-submission metrics on the challenge test set generalize to real-world clinical deployment across varied scanners, patient populations, and treatment planning systems without additional site-specific tuning or validation.
SynthRAD2025 shows deep learning produces synthetic CTs with MAE 48-65 HU and high dosimetric gamma passing rates for radiotherapy, performing better on CBCT-to-CT than MRI-to-CT tasks.
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| First computed | 2026-05-18T02:44:23.646454Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
a1722baf277cc1df747143c05eb0c6378e68758b9eb7b04222ae12fcd280180b
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/UFZCXLZHPTA565DRIPAF5MGGG6 \
| 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: a1722baf277cc1df747143c05eb0c6378e68758b9eb7b04222ae12fcd280180b
Canonical record JSON
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