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ARXIV:2605.13555 · MEDICAL AI · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.13555MEDICAL AISUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHViktor Rogowski · Maarten L. Terpstra · Niklas Wahl · Florian Kamp · Erik van der Bijl · Arthur Jr. Galapon · +27 at arXiv
A benchmark for synthetic CT generation in radiotherapy, showing deep learning can produce clinically relevant images but dose-based evaluation is essential.
Opportunity summary
Pain A benchmark for synthetic CT generation in radiotherapy, showing deep learning can produce clinically relevant images but dose-based evaluation is essential.
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A benchmark for synthetic CT generation in radiotherapy, showing deep learning can produce clinically relevant images but dose-based evaluation is essential. Repeated CT acquisitions impose radiation exposure and logistical burdens, MRI lacks electron density,…
Radiation therapy (RT) requires precise dose delivery over multiple fractions, with CT fundamental for treatment planning due to its electron density information. Repeated CT acquisitions impose radiation exposure and logistical burdens, MRI lacks electron…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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…
Medical AI moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
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A benchmark for synthetic CT generation in radiotherapy, showing deep learning can produce clinically relevant images but dose-based evaluation is essential.
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10.48550/arXiv.2605.13555A benchmark for synthetic CT generation in radiotherapy, showing deep learning can produce clinically relevant images but dose-based evaluation is essential.
Abstract
Radiation therapy (RT) requires precise dose delivery over multiple fractions, with CT fundamental for treatment planning due to its electron density information. Repeated CT acquisitions impose radiation exposure and logistical burdens, MRI lacks electron density, and cone-beam CT (CBCT) requires correction for dose calculation. Synthetic CT (sCT) generation addresses these by converting MRI or CBCT into CT-equivalent images with accurate Hounsfield Unit (HU) values, enabling MRI-only RT and CBCT-based adaptive workflows. Building on SynthRAD2023, SynthRAD2025 benchmarked sCT methods on 2,362 patients from five European centers across head and neck, thorax, and abdomen. Two tasks: MRI-to-CT (890 cases) and CBCT-to-CT (1,472 cases), evaluated via image similarity (MAE, PSNR, MS-SSIM), segmentation (Dice, HD95), and dosimetric metrics from photon and proton plans. With 803 participants and 12/13 valid submissions, Task 1 top performance reached MAE $64.8\pm21.3$ HU, PSNR $\sim$30 dB, MS-SSIM $\sim$0.936, Dice 0.79, photon $γ_{2\%/2\text{mm}}>98\%$, proton $γ\approx85\%$. Task 2 improved: MAE $48.3\pm13.4$ HU, PSNR 32.6 dB, MS-SSIM 0.968, Dice 0.86, photon $γ>99\%$, proton $γ\approx89\%$. Strong image--segmentation correlations ($ρ=0.78$--$0.79$) but moderate dose correlations confirmed image quality is insufficient as a dosimetric surrogate. Head-and-neck cases were most consistent; thoracic and abdominal cases showed greater variability. Residual errors at tissue interfaces propagate along beam paths, affecting proton dose more than photon. 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.
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PROBLEM
A benchmark for synthetic CT generation in radiotherapy, showing deep learning can produce clinically relevant images but dose-based evaluation is essential. Repeated CT acquisitions impose radiation exposure and logistical burdens, MRI lacks electron density, and cone-beam CT (...
METHOD
Radiation therapy (RT) requires precise dose delivery over multiple fractions, with CT fundamental for treatment planning due to its electron density information. Repeated CT acquisitions impose radiation exposure and logistical burdens, MRI lacks electron density, and cone-beam...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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 essenti...
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Medical AI moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
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A benchmark for synthetic CT generation in radiotherapy, showing deep learning can produce clinically relevant images but dose-based evaluation is essential. Repeated CT acquisitions impose radiation exposure and logistical burdens, MRI lacks electron density, and cone-beam CT (CBCT) requires correction for dose calculation.
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partial
Radiation therapy (RT) requires precise dose delivery over multiple fractions, with CT fundamental for treatment planning due to its electron density information. Repeated CT acquisitions impose radiation exposure and logistical burdens, MRI lacks electron density, and cone-beam CT (CBCT) requires correction for dose calculation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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. Code availability is flagged in the production record; the public repository link still needs proof alignment.
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partial
Medical AI moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A benchmark for synthetic CT generation in radiotherapy, showing deep learning can produce clinically relevant images but dose-based evaluation is essential.
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