SU-E-T-95: Imaging Protocol Investigations with a Fan-Beam Optical CT Scanner for 3D Dosimetry
Bibliographic record
Abstract
Purpose: To investigate data acquisition protocol choices and their effects on image noise for a prototype fan-beam optical CT scanner intended for evaluating 3D dosimeters. Methods: The prototype scans dosimeters submerged in a refractive index-matching bath. For all tests, uniform samples of scattering solution (synthetic polymer in water) were housed in cylindrical containers (1-litre, 95 mm diameter) and scanned in a water bath. for reference data (I_o), options evaluated were: i) a projection through the bath, ii) an average projection through a “blank” (water-filled container), and iii) an entire sinogram through a “blank”. Scan parameters considered were: i) temporal delay between I_o and I scans, ii) number of projections per rotation, iii) number of samples averaged per projection, and iv) vertical displacement between I_o and I slices. Images were reconstructed using filtered backprojection. Relative noise (st.dev./mean) in a consistent region of interest was used to quantify image quality. Results: Using a reference scan through a “blank” as opposed to one through only a water bath eliminated major ring artefacts. Furthermore, using a full I_o sinogram through a “blank” instead of an averaged profile considerably reduced noise (22.4% to 3.1%). Minimal increases in noise were seen when delaying scans (3% to 5%, one hour later). Increases in noise were seen with fewer than 360 projections per rotation; with > 360 projections, no significant reductions in noise were seen (up to 1800). No benefit was found by acquiring multiple samples per projection. Significant noise increase was seen when I_o and I slices were displaced by even 1 mm (2.6% to 15.5%). Conclusions: Noise reduction is optimized when comparing scan sinograms ray-by-ray to full sinograms of a “blank”. Main sources of image noise were attributed to variations in the surface quality of the containers used. Funding from the Canadian Institutes of Health Research and the University of Victoria supported portions of this work.
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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".