Structural Guidance in Stacked Generative Diffusion Model: Synthesizing Head and Neck CT from MRI in Radiotherapy Planning
Bibliographic record
Abstract
Head and neck radiotherapy often combines a patient's MRI, showing soft tissue contrast, with a pre-treatment CT allowing for dosimetry planning. Synthesizing missing CT data from available MR images minimizes radiation exposure, and facilitates adaptive re-planning. We propose a generative diffusion model that synthesizes CT images of tumors from available MR modalities, incorporating structural guidance within a stacked diffusion framework. The model utilizes two stacked denoising diffusion probabilistic models (DDPMs). The first is a structure image generator, producing structural representations of CT images from the corresponding MRI inputs. These representations are then utilized by a second contextual image DDPM, which leverages both the original MRI and the generated structural representations as an augmented multi-channel input to improve the synthesis of the CT images. Our training employs a variational inference approach that combines a lower variational bound loss with a mean absolute error loss, leveraging both structural and contextual features. Evaluated on the Head and Neck Organ-at-Risk Multi-Modal dataset (HaN-Seg), our model outperforms recent MR-to-CT generative diffusion models, achieving a multiscale structure similarity index (multiscale-SSIM) of 0.85 ± 0.08, a mean absolute error (MAE) of 0.09 ± 0.06, and a peak signal-to-noise ratio (PSNR) of 22.05 ± 1.83. Additionally, the model achieved the highest probability rand index (PRI) score of 0.83 ± 0.04 with a Dice score of 0.75 ± 0.07, and a global consistency error (GCE) of 0.16 ± 0.05 on segmented tumor area of synthetic sCT images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".