An anatomic clutter phantom for lung-ventilation-imaging studies
Bibliographic record
Abstract
Xenon-enhanced, dual-energy x-ray radiography has been proposed for imaging of lung ventilation. It is important to assess the ability of dual-energy subtraction to suppress anatomic noise associated with lung parenchyma. Anatomic noise in thoracic radiography obeys an inverse power law and there exist imaging phantoms that mimic this power law. Such phantoms are based on a random, tight packing of solid acrylic spheres and are not suitable for lung ventilation studies. We developed a phantom based on randomly-packed, hollow acrylic cylinders with inner diameters of 1.59 cm, wall thicknesses of 0.16 cm and lengths of 1.59, 1.27, 0.95, 0.64 or 0.32 cm. The number of segments of each length was chosen to approximately match the volume of space occupied by each set of segments. Measurements of the effective density of the packed cylinders yielded ~0.26 g cm-3. A randomly-packed-sphere phantom was also constructed as a reference. Both phantoms were imaged using a flat-panel detector at tube voltages of 50 kV to 150 kV. A power-law model (NPS ∝ κ/|u|β) was fit to the anatomic noise power spectra. The β-value of the cylinder phantom was within 1/5 of that of the sphere phantom, although both phantoms yielded power-law parameters ranging from 2.0 to 2.4, which is lower than that reported in the literature. The κ-value of the cylinder phantom was ~1.1 times that of the sphere phantom. We conclude that the cylinder-based clutter phantom, with some modifications, can be used to simulate the anatomic noise power spectrum in thoracic radiography.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".