A quality assurance phantom for the performance evaluation of volumetric micro-CT systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it