Feasibility of ultra‐low‐dose volumetric 4D‐CT with frame averaging for high‐fidelity respiratory motion assessment and improved image quality
Bibliographic record
Abstract
Abstract Background Precise modeling of respiratory‐induced tumor motion is crucial for radiotherapy planning, particularly in the treatment of thoracic and abdominal malignancies. While four‐dimensional computed tomography (4D‐CT) remains the clinical standard of care for motion assessment, it lacks the capability for true cine imaging of tumor motion. In contrast, ultra‐low‐dose volumetric 4D‐CT (v4D‐CT) enables true cine acquisition by leveraging an extended axial field of view and distributing radiation dose over a prolonged acquisition time. However, this technique typically yields suboptimal, non‐diagnostic image quality. Purpose This study evaluates a novel ultra‐low‐dose v4D‐CT imaging protocol designed to accurately capture tumor motion and enhance image quality through retrospective frame averaging. Methods A 30‐s continuous ultra‐low‐dose scan was performed at a single couch position using a wide‐field volumetric CT system. A Catphan 504 phantom (for image quality assessment) and a QUASAR motion phantom (for motion analysis), with extension rings added to simulate large patient anatomy, were imaged at 20 mA. Frame averaging was performed in both image space (IS‐FA) and projection space (PS‐FA) to improve image quality. Key image quality metrics, including CT number uniformity, noise, spatial resolution, signal‐difference‐to‐noise ratio (SDNR), and Hounsfield Unit (HU) accuracy, were assessed using Catphan modules and benchmarked against reference scans at 300 and 700 mA. Motion tracking and segmentation accuracy were evaluated using the QUASAR phantom's spherical inserts (diameters: 0.5, 1.0, 2.0, and 3.0 cm) with simulated 1 cm peak‐to‐peak motion from a patient signal. Accuracy was quantified using the coefficient of determination ( r 2 ) and DICE similarity coefficients. Results Frame averaging substantially improved all evaluated image quality metrics. IS‐FA achieved superior noise suppression and contrast enhancement, albeit with some degradation in spatial resolution. In contrast, PS‐FA preserved spatial resolution and yielded noise characteristics statistically indistinguishable from reference scans ( p > 0.4), closely approximating image quality at 300 and 700 mA. Tumor insert visibility and motion tracking were achievable at 20 mA, with high correlation to ground truth ( r 2 > 0.99) and segmentation accuracy (DICE > 0.95) for inserts ≥2.0 cm in diameter. Conclusion Ultra‐low‐dose v4D‐CT combined with retrospective frame averaging provides accurate respiratory motion characterization and significantly enhanced image quality. This technique offers a viable, non‐invasive alternative for patient‐specific motion modeling in radiotherapy planning.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".