Achieving high-resolution soft-tissue imaging with cone-beam CT: a two-pronged approach for modulation of x-ray fluence and detector gain
Bibliographic record
Abstract
Cone-beam computed tomography (CBCT) presents a highly promising and challenging advanced application of flat-panel detectors (FPDs). The great advantage of this adaptable technology is in the potential for sub-mm 3D spatial resolution in combination with soft-tissue detectability. While the former is achieved naturally by CBCT systems incorporating modern FPD designs (e.g., 200 - 400 um pixel pitch), the latter presents a significant challenge due to limitations in FPD dynamic range, large field of view, and elevated levels of x-ray scatter in typical CBCT configurations. We are investigating a two-pronged strategy to maximizing soft-tissue detectability in CBCT: 1) front-end solutions, including novel beam modulation designs (viz., spatially varying compensators) that alleviate detector dynamic range requirements, reduce x-ray scatter, and better distribute imaging dose in a manner suited to soft-tissue visualization throughout the field of view; and 2) back-end solutions, including implementation of an advanced FPD design (Varian PaxScan 4030CB) that features dual-gain and dynamic gain switching that effectively extends detector dynamic range to 18 bits. These strategies are explored quantitatively on CBCT imaging platforms developed in our laboratory, including a dedicated CBCT bench and a mobile isocentric C-arm (Siemens PowerMobil). Pre-clinical evaluation of improved soft-tissue visibility was carried out in phantom and patient imaging with the C-arm device. Incorporation of these strategies begin to reveal the full potential of CBCT for soft-tissue visualization, an essential step in realizing broad utility of this adaptable technology for diagnostic and image-guided procedures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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".