Towards real‐time conformal palliative treatment of spine metastases: A deep learning approach for Hounsfield Unit recovery of cone beam CT images
Bibliographic record
Abstract
BACKGROUND: The extension of onboard cone-beam CT (CBCT) imaging for real-time treatment planning is constrained by limitations in image quality. Synthetic CT (sCT) generation using deep learning provides a potential solution to these limitations. PURPOSE: This study was dedicated to creating a model capable of rapidly generating sCT images from CBCT scans, specifically for the entire spine. This work aims to be a step towards a CT simulation-free workflow by using onboard imaging for real-time palliative radiotherapy treatments for patients with spinal metastases. METHODS: Using CBCT and planning fan-beam CT images from 220 patients, we developed and validated a two-stage sCT generation model. The initial stage used a conditional generative adversarial network (GAN) to minimize streaking artifacts in CBCT images, using 7400 images for training and 1000 for validation. The second stage used a cycle-consistent GAN to produce sCT images, training on 14,700 images and validating on 500 images. The quality of the sCT images was evaluated quantitatively using a distinct dataset from 33 patients who received same-day palliative radiotherapy for spinal metastases. RESULTS: 20 HU. Dosimetric comparison for a subset of 20 patients indicated that the mean dose discrepancy for sCT-based calculations was lower than CBCT-based calculations by 4.5%, with the gamma (2 mm/2%) pass rate increasing by 34% on average. CONCLUSIONS: This study demonstrates how a two-stage network facilitates CBCT-based sCT generation across the entire spine without prior CT knowledge, improving HU accuracy and potentially enabling highly-conformal palliative treatment planning for spinal metastases in real time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".