A comparison of x-ray detectors for mouse CT imaging
Bibliographic record
Abstract
There is significant interest in using computed tomography (CT) for in vivo imaging applications in mouse models of disease. Most commercially available mouse x-ray CT scanners utilize a charge-coupled device (CCD) detector coupled via fibre optic taper to a phosphor screen. However, there has been little research to determine if this is the optimum detector for the specific task of in vivo mouse imaging. To investigate this issue, we have evaluated four detectors, including an amorphous selenium (a-Se) detector, an amorphous silicon (a-Si) detector with a gadolinium oxysulphide (GOS) screen, a CCD with a 3:1 fibre taper and a GOS screen, and a CCD with a 2:1 fibre taper and both GOS and thallium-doped caesium iodide (CsI:Tl) screens. The detectors were evaluated by measuring the modulation transfer function (MTF), noise power spectrum (NPS), detective quantum efficiency (DQE), stability over multiple exposures, and noise in reconstructed CT images. The a-Se detector had the best MTF and the highest DQE (0.6 at 0 lp mm(-1)) but had the worst stability (45% reduction after 2000 exposure frames). The a-Si detector and the CCD with the 3:1 fibre, both of which used the GOS screen, had very similar performance with a DQE of approximately 0.30 at 0 lp mm(-1). For the CCD with the 2:1 fibre, the CsI:Tl screen resulted in a nearly two-fold improvement in DQE over the GOS screen (0.4 versus 0.24 at 0 lp mm(-1)). The CCDs both had the best stability, with less than a 1% change in pixel values over multiple exposures. The pixel values of the a-Si detector increased 5% over multiple exposures due to the effects of image lag. Despite the higher DQE of the a-Se detector, the reconstructed CT images acquired with the a-Si detector had lower noise levels, likely due to the blurring effects from the phosphor screen.
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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 | 0.000 | 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 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".