Comparison of air space measurement imaged by CT, small-animal CT, and hyperpolarized Xe MRI
Bibliographic record
Abstract
Lung disease is the third leading cause of death in the western world. Lung air volume measurements are thought to be early indicators of lung disease and markers in pharmaceutical research. The purpose of this work is to develop a lung phantom for assessing and comparing the quantitative accuracy of hyperpolarized xenon 129 magnetic resonance imaging (HP <sup>129</sup><i>Xe</i> MRI), conventional computed tomography (HRCT), and highresolution small-animal CT (<i>μ</i>CT) in measuring lung gas volumes. We developed a lung phantom consisting of solid cellulose acetate spheres (1, 2, 3, 4 and 5 mm diameter) uniformly packed in circulated air or HP <sup>129</sup><i>Xe</i> gas. Air volume is estimated based on simple thresholding algorithm. Truth is calculated from the sphere diameters and validated using <i>μ</i>CT. While this phantom is not anthropomorphic, it enables us to directly measure air space volume and compare these imaging methods as a function of sphere diameter for the first time. HP <sup>129</sup><i>Xe</i> MRI requires partial volume analysis to distinguish regions with and without <sup>129</sup><i>Xe</i> gas and results are within %5 of truth but settling of the heavy <sup>129</sup><i>Xe</i> gas complicates this analysis. Conventional CT demonstrated partial-volume artifacts for the 1mm spheres. <i>μ</i>CT gives the most accurate air-volume results. Conventional CT and HP <sup>129</sup><i>Xe</i> MRI give similar results although non-uniform densities of <sup>129</sup><i>Xe</i> require more sophisticated algorithms than simple thresholding. The threshold required to give the true air volume in both HRCT and <i>μ</i>CT, varies with sphere diameters calling into question the validity of thresholding method.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".