HyperSight CBCT image quality and metal artifact reduction for adaptive head and neck radiotherapy: Results from a prospective clinical trial
Bibliographic record
Abstract
BACKGROUND: Accurate Hounsfield units (HU) are critical for dose calculation and anatomical visualization, but are often affected by dental artifacts in head and neck (H&N) cancer patients. The HyperSight cone-beam computed tomography (CBCT) platform provides improved image quality over previous CBCT platforms and offers metal artifact reduction (iCBCT MAR) reconstruction. PURPOSE: This study evaluates the quality of HyperSight CBCT images compared to current clinical standards: TrueBeam CBCT for image guidance and fan-beam CT (FBCT) from a CT simulator for treatment planning, using images captured during H&N cancer treatment. METHODS: Images for 30 H&N cancer patients were acquired on a HyperSight CBCT, conventional TrueBeam and FBCT, with 24 patients exhibiting metal dental artifacts. The HyperSight images were reconstructed using iCBCT MAR and iterative (iCBCT Acuros) algorithms. The four image sets were rigidly registered and compared using the artifact index (AI) measured in the oral cavity and the percentile range (PR) measured in the oral cavity, brain, brainstem and eyes to assess image non-uniformity. The HU accuracy was calculated relative to FBCT (baseline) for soft tissues (oral cavity, brainstem, submandibular and parotid glands), and bone (mandible). The contrast relative to baseline was evaluated between the oral cavity and nearby structures. Image-based metrics were computed relative to FBCT including structural similarity index measure (SSIM), mean-square error (MSE) and peak signal-to-noise ratio (PSNR). RESULTS: The HyperSight iCBCT MAR images showed a significant reduction in AI values compared to the other images (p < 0.0004), but higher PR values indicating decreased HU uniformity compared to HyperSight iCBCT Acuros and FBCT (p < 0.0002). The soft-tissue HU and contrast values were significantly closer to baseline in both HyperSight images compared to TrueBeam (HU: p < 0.001, contrast: p < 0.001). For soft-tissue the HU mean absolute deviation (MAD) from baseline was 16 ± 10 HU for HyperSight iCBCT Acuros, 15 ± 10 HU for HyperSight iCBCT MAR, and 35 ± 22 HU for TrueBeam. For bone, the HU MAD from baseline was 153 ± 233 HU, 185 ± 268 HU, and 214 ± 212 HU, respectively. The HyperSight iCBCT Acuros algorithm achieved significantly superior SSIM, MSE, and PSNR metrics compared to TrueBeam and HyperSight iCBCT MAR in regions with large amounts of bone and air. CONCLUSIONS: HyperSight iCBCT MAR significantly reduced artifacts compared to HyperSight iCBCT Acuros, TrueBeam and FBCT, making it particularly beneficial for patients with metal implants. Both HyperSight reconstructions demonstrated improved soft-tissue HU accuracy and contrast compared to TrueBeam, however the iCBCT Acuros algorithm may be preferred when metal-induced artifacts are not a concern. These results support the suitability of HyperSight images in adaptive treatment workflows requiring accurate image quality, even with severe metal artifacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".