WE-E-330D-05: Investigation of Imaging Performance and Acquisition Technique for a New Dual-Energy Chest Imaging System
Bibliographic record
Abstract
Purpose: A novel, high-performance, cardiac-gated dual-energy (DE) chest system is under development in our lab. This paper investigates the influence of key image acquisition technique parameters (viz., selection of kVp, filtration, and dose) on DE imaging performance. Method and Materials: Experiments were conducted on a DE imaging bench with a custom-built phantom containing simulated lung nodules of varying contrast. Performance was quantified in terms of nodule contrast-noise ratio (CNRDE) in DE ‘tissue-only’ images. Low- and high-kVp were varied from 60–90 kVp and 120–150 kVp, respectively. Differential added filtration in low- and high-kVp projections was analyzed in terms of soft-tissue CNRDE both theoretically across the entire Periodic Table (Z=1−92) and experimentally for specific material types (Al, Ce, Cu, and Ag). Allocation of dose (defined A=ESDlow/ESDhigh) between low- and high-energy projections was analyzed at various levels of total entrance surface dose, ESD, over a broad range of allocation. Results: The results provide valuable guidance of technique selection for high-performance DE imaging. Optimal performance was achieved at a technique of [60/130] kVp, increasing soft-tissue CNRDE by 32% compared to [90/120] kVp. Differential added filtration [0.2 mm Ce / 0.6 mm Ag] increased soft-tissue CNRDE by 21% compared to the undifferentiated case ([1 mm Al / 1 mm Al]). Dose allocation was found to have significant influence on performance, with CNRDE increasing by more than ∼30% for A<1 compared to higher A>3 (with optima suggested in the range A∼0.3–0.5). Conclusion: Knowledgeable selection of kVp pairs, differential added filtration, and dose allocation provide significant increase in the soft-tissue CNR of DE images compared to conventional or sub-optimal techniques. Quantitative theoretical and experimental evaluation demonstrates the importance of optimized acquisition techniques for high-performance DE imaging and guides the implementation of a novel DE imaging system under development for pre-clinical imaging trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".