Optimizing dual‐energy x‐ray parameters for the ExacTrac clinical stereoscopic imaging system to enhance soft‐tissue imaging
Bibliographic record
Abstract
PURPOSE: Stereoscopic x-ray image guided radiotherapy for lung tumors is often hindered by bone overlap and limited soft-tissue contrast. This study aims to evaluate the feasibility of dual-energy imaging techniques and to optimize parameters of the ExacTrac stereoscopic imaging system to enhance soft-tissue imaging for application to lung stereotactic body radiation therapy. METHODS: Simulated spectra and a physical lung phantom were used to optimize filter material, thickness, tube potentials, and weighting factors to obtain bone subtracted dual-energy images. Spektr simulations were used to identify material in the atomic number range (3-83) based on a metric defined to separate spectra of high and low-energies. Both energies used the same filter due to time constraints of imaging in the presence of respiratory motion. The lung phantom contained bone, soft tissue, and tumor mimicking materials, and it was imaged with a filter thickness in the range of (0-0.7) mm and a kVp range of (60-80) for low energy and (120,140) for high energy. Optimal dual-energy weighting factors were obtained when the bone to soft-tissue contrast-to-noise ratio (CNR) was minimized. Optimal filter thickness and tube potential were achieved by maximizing tumor-to-background CNR. Using the optimized parameters, dual-energy images of an anthropomorphic Rando phantom with a spherical tumor mimicking material inserted in his lung were acquired and evaluated for bone subtraction and tumor contrast. Imaging dose was measured using the dual-energy technique with and without beam filtration and matched to that of a clinical conventional single energy technique. RESULTS: Tin was the material of choice for beam filtering providing the best energy separation, non-toxicity, and non-reactiveness. The best soft-tissue-weighted image in the lung phantom was obtained using 0.2 mm tin and (140, 60) kVp pair. Dual-energy images of the Rando phantom with the tin filter had noticeable improvement in bone elimination, tumor contrast, and noise content when compared to dual-energy imaging with no filtration. The surface dose was 0.52 mGy per each stereoscopic view for both clinical single energy technique and the dual-energy technique in both cases of with and without the tin filter. CONCLUSIONS: Dual-energy soft-tissue imaging is feasible without additional imaging dose using the ExacTrac stereoscopic imaging system with optimized acquisition parameters and no beam filtration. Addition of a single tin filter for both the high and low energies has noticeable improvements on dual-energy imaging with optimized parameters. Clinical implementation of a dual-energy technique on ExacTrac stereoscopic imaging could improve lung tumor visibility.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".