Sci‐Thur AM: YIS – 07: Optimizing dual‐energy x‐ray parameters using a single filter for both high and low‐energy images to enhance soft‐tissue imaging
Bibliographic record
Abstract
Objective: To optimize dual‐energy parameters of ExacTrac stereoscopic x‐ray imaging system for lung SBRT patients Methods: Simulated spectra and a lung phantom were used to optimize filter material, thickness, kVps, and weighting factors to obtain bone subtracted dual‐energy images. Spektr simulations were used to identify material in the atomic number (Z) range [3–83] based on a metric defined to separate spectrums of high and low energies. Both energies used the same filter due to time constraints of image acquisition in lung SBRT imaging. A lung phantom containing bone, soft tissue, and a tumor mimicking material was imaged with filter thicknesses range [0–1] mm and kVp range [60–140]. A cost function based on contrast‐to‐noise‐ratio of bone, soft tissue, and tumor, as well as image noise content, was defined to optimize filter thickness and kVp. Using the optimized parameters, dual‐energy images of anthropomorphic Rando phantom were acquired and evaluated for bone subtraction. Imaging dose was measured with dual‐energy technique using tin filtering. Results: Tin was the material of choice providing the best energy separation, non‐toxicity, and non‐reactiveness. The best soft‐tissue‐only image in the lung phantom was obtained using 0.3 mm tin and [140, 80] kVp pair. Dual‐energy images of the Rando phantom had noticeable bone elimination when compared to no filtration. Dose was lower with tin filtering compared to no filtration. Conclusions: Dual‐energy soft‐tissue imaging is feasible using ExacTrac stereoscopic imaging system utilizing a single tin filter for both high and low energies and optimized acquisition parameters.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".