On the comparison of two NIPAM gel dosimeters with high resolution 3D MRI sequences
Bibliographic record
Abstract
Abstract Polymer gel dosimeters allow the measurement of a dose distribution in 3 dimensions. Irradiation induces polymerization reactions in the long chains of the gel molecules, which is characterized by a change in the transverse relaxation time T2. Gel dosimetry is mainly advantageous for its high resolution, but can also be expensive. The main goal of this project is to compare two normoxic polymer gel recipes made with NIPAM as monomer. The analysis is based on the quality of the images obtained for a fixed MRI scan time and a comparative cost of analysis of each 1L phantom. The ΔT2 maps are obtained by scanning the gels before and after irradiation with 3D sequences and the DESPOT2 technique is used to reconstruct the T2 maps. The phantoms were irradiated with 8 photon beams of 6 MV with a fixed 250 MU, 4 at two opposite sides of the jar (AP-PA configuration), to obtain 4 dose distributions of 4.7 Gy, 8.9 Gy, 13.2 Gy and 17.3 Gy. The SNR is then determined in the ΔT2 maps in function of the dose step and the concentration of NIPAM in the phantom. The first gel phantom is made with 15% NIPAM and has a total cost of $1561.65, including products and MRI scan costs. The SNR obtained for the 4 dose steps are 10.02, 31.18, 45.12 and 37.54. The contrast between the T2 before and after the irradiation in the 4 regions of dose are 0.45, 0.66, 0.78 and 0.85. The second phantom contained 5% NIPAM, cost 1186.85$ and the SNR obtained are 16.80, 27.08, 30.11 and 26.17 and the ΔT2 are of 0.61, 0.75, 0.79 and 0.77 for the 4 dose steps respectively. The comparison of the two recipes has shown that the increase of NIPAM concentration does not allow a significant increase in image quality for MRI. The recipe with 5% of NIPAM has a lower dynamic range but a better sensitivity at lower dose and is less expensive than the one with 15% of NIPAM. The results also show that by increasing the NIPAM concentration 3-fold, the cost of a 1L phantom increases by 30%.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".