Optimization of dual‐energy imaging systems using generalized NEQ and imaging task
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dual-energy (DE) imaging is a promising advanced application of flat-panel detectors (FPDs) with a potential host of applications ranging from thoracic and cardiac imaging to interventional procedures. The performance of FPD-based DE imaging systems is investigated in this work by incorporating the noise-power spectrum associated with overlying anatomical structures ("anatomical noise" modeled according to a 1/f characteristic) into descriptions of noise-equivalent quanta (NEQ) to yield the generalized NEQ (GNEQ). Signal and noise propagation in the DE imaging chain is modeled by cascaded systems analysis. A Fourier-based description of the imaging task is integrated with the GNEQ to yield a detectability index used as an objective function for optimizing DE image reconstruction, allocation of dose between low- and high-energy images, and selection of low- and high-kVp. Optimal reconstruction and acquisition parameters were found to depend on dose; for example, optimal kVp varied from [60/150] kVp at typical radiographic dose levels (approximately 0.5 mGy entrance surface dose, ESD) but increased to [90/150] kVp at high dose (ESD approximately 5.0 mGy). At very low dose (ESD approximately 0.05 mGy), detectability index indicates an optimal low-energy technique of 60 kVp but was largely insensitive to the choice of high-kVp in the range 120-150 kVp. Similarly, optimal dose allocation, defined as the ratio of low-energy ESD and the total ESD, varied from 0.2 to 0.4 over the range ESD=(0.05-5.0) mGy. Furthermore, two applications of the theoretical framework were explored: (i) the increase in detectability for DE imaging compared to conventional radiography; and (ii) the performance of single-shot vs double-shot DE imaging, wherein the latter is found to have a DQE approximately twice that of the former. Experimental and theoretical analysis of GNEQ and task-based detectability index provides a fundamental understanding of the factors governing DE imaging performance and offers a framework for system design and optimization.
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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 it