TH‐D‐L100J‐08: Imaging Performance of a Mobile Cone‐Beam CT C‐Arm for Image‐Guided Interventions
Bibliographic record
Abstract
Purpose: To characterize the imaging performance of a mobile cone‐beam CT (CBCT) C‐arm for image‐guided interventions. This work reports on 3D image quality of a flat‐panel detector with multiple gain modes (Varian PaxScan 4030CB), radiation dose, and robust methods for geometric calibration and artifact management. Method and Materials: A prototype imaging system based on a mobile C‐arm (Siemens PowerMobil) has been developed to provide flat‐panel CBCT. Three readout modes (fixed‐, dual‐, and dynamic‐gain) were evaluated in CBCT phantom images across a range of dose (0.6–18.8 mGy). An analytic (non‐iterative) geometric calibration method capable of determining all nine degrees of freedom in source‐detector geometry was implemented. Image artifacts associated with x‐ray scatter and lateral truncation were characterized, and methods for artifact management (scatter estimation and projection extrapolation, respectively) were evaluated. Results: CBCT images exhibit soft‐tissue visibility (∼20 HU) and high spatial resolution (∼1 mm) at dose (∼10 mGy) sufficiently low as to permit repeat intraoperative imaging. Dynamic‐gain readout demonstrated the highest level of soft‐tissue and bony‐detail visibility across all doses, whereas fixed‐gain was degraded at high dose due to pixel saturation, and dual‐gain was degraded due to image noise. The C‐arm exhibits large geometric non‐idealities (>15 mm departure from semicircular orbit) due to mechanical flex; however, the geometric calibration restored image quality (e.g., 0.77 mm FWHM) and was reproducible to sub‐pixel precision. Lateral truncation artifacts were effectively minimized via mixed linear‐exponential extrapolation of projections at the detector edges, and x‐ray scatter was managed to a large extent by subtraction of 2D scatter fluence estimates based on the measured detector signal (patient thickness). Conclusion: The prototype C‐arm demonstrates sufficient image quality for guidance at doses low enough for repeat intraoperative imaging. The C‐arm is currently being deployed in patient protocols ranging from brachytheraphy to chest, breast, spine and head‐and‐neck surgery.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".