MicroCT scanner performance and considerations for vascular specimen imaging
Bibliographic record
Abstract
Obtaining three-dimensional geometrical data of vascular systems is of major importance to a number of research areas in medicine and biology. Examples are the characterization of tumor vasculature, modeling blood flow, or genetic effects on vascular development. The performance of the General Electric Medical Systems MS8 microCT scanner is examined in the context of these applications. The system is designed to acquire high-resolution images of specimens up to 5 cm in diameter. A maximum resolution of 38 lp/mm at the 10% modulation transfer function level or 22 microm full width at half maximum of the plane spread function can be achieved with 8.5 microm voxels and a 17 mm field of view. Three different contrast agents are discussed and applied for imaging of small animal vasculature: corrosion casting material Batson's No. 17 with an added lead pigment, silicon rubber MICROFIL MV122, and a suspension of barium sulfate (Baritop) in gelatin. Contrast for all of these agents was highly variable in different vessels as well as within the same vessel. Imaging of PMMA tubing filled with MICROFIL shows that even vessels below 20 microm in diameter are detectable and that diameter estimation of vessels based on thresholding is possible with a precision of 2-3 pixels.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".