SU‐C‐220‐06: Comparison of the Absorption Coefficient and Diffraction Enhanced Imagingˈˈs Apparent Absorption Coefficient of Lung Tissue
Bibliographic record
Abstract
Purpose: Diffraction enhanced imaging (DEI) generates image contrast in a fundamentally different way than absorption x‐ray imaging—the refraction and extinction of x‐rays yield unique contrast. The purpose of this study was to measure the apparent absorption coefficient of the lung tissue in normal rats and to quantify the difference in contrast between DEIˈs peak image and radiography. Methods: Chest radiographs and DEI peak images (analyzer crystal aligned to the Bragg peak) were acquired of anesthetized and paralyzed Sprague Dawley rats (n=14) at Beamline X15A of the National Synchrotron Light Source at an x‐ray energy of 60 keV. The images were acquired in the anteroposterior (AP) direction. Intensity measurements were used to calculate an approximate apparent absorption coefficient of rat lung tissue. Measurements of the transmitted intensity of both the radiograph and DEI peak images were then used to calculate contrast within the lung. Results: The measured apparent absorption coefficient for the inflated lungs was 0.86 +/− 0.15 cm̂−1. To put this in context, the absorption coefficient of air, water and bone at 60 keV are 0.0002 cm̂−1, 0.21 cm̂−1 and 0.50 cm̂−1, respectively. Thus, the DEI contrast generated by the lung is about two times that generated by the same thickness of bone. DEI showed a significant gain in contrast over radiography (C_DEI/C_Rad) of 1.7 +/− 0.6. Conclusions: DEI generates 70% greater lung contrast than radiography at 60 keV. The large difference in the apparent absorption coefficient of DEI and the absorption coefficient of conventional radiography shows that DEI can be used to either enhance lung contrast for the same radiation dose, or could be used to substantially reduce the required radiation dose for a chest x‐ray while maintaining the same contrast‐to‐noise ratio. Drs. Connor, Parham, Pisano and Zhong are equity holders in NextRay, Inc. Dr. Pisano is on the Board of Directors at NextRay, Inc. Drs. Connor, Parham and Zhong are consultants to NextRay, Inc.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".