A quality assurance phantom for the performance evaluation of volumetric micro-CT systems
Bibliographic record
Abstract
Small-animal imaging has recently become an area of increased interest because more human diseases can be modeled in transgenic and knockout rodents. As a result, micro-computed tomography (micro-CT) systems are becoming more common in research laboratories, due to their ability to achieve spatial resolution as high as 10 microm, giving highly detailed anatomical information. Most recently, a volumetric cone-beam micro-CT system using a flat-panel detector (eXplore Ultra, GE Healthcare, London, ON) has been developed that combines the high resolution of micro-CT and the fast scanning speed of clinical CT, so that dynamic perfusion imaging can be performed in mice and rats, providing functional physiological information in addition to anatomical information. This and other commercially available micro-CT systems all promise to deliver precise and accurate high-resolution measurements in small animals. However, no comprehensive quality assurance phantom has been developed to evaluate the performance of these micro-CT systems on a routine basis. We have designed and fabricated a single comprehensive device for the purpose of performance evaluation of micro-CT systems. This quality assurance phantom was applied to assess multiple image-quality parameters of a current flat-panel cone-beam micro-CT system accurately and quantitatively, in terms of spatial resolution, geometric accuracy, CT number accuracy, linearity, noise and image uniformity. Our investigations show that 3D images can be obtained with a limiting spatial resolution of 2.5 mm(-1) and noise of +/-35 HU, using an acquisition interval of 8 s at an entrance dose of 6.4 cGy.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".