Measuring the effect of CZT detector materials on MRI field homogeneity
Bibliographic record
Abstract
There has been significant interest in the use of semiconductor detectors such as cadmium zinc telluride (CZT) for SPECT/MRI and PET/MRI due to their ability to operate in high magnetic fields. However, the effect of these materials on magnetic field homogeneity must be considered when designing an MR-compatible gamma camera. Field maps were generated for several materials considered for an MR-compatible gamma camera to determine the extent of the shift in magnetic field. The materials used were CZT, Al, carbon fiber, printed circuit boards, thermoelectric cooler, Pb and a composite material consisting largely of tungsten. A lipid phantom filled with a mixture of 50% canola oil and 50% water (~9 cm × ~9 cm × 6 cm) was imaged with and without the materials placed adjacent to the phantom/foot ankle coil. Data were acquired with the materials individually and as a combined system with and without power. MRI phase images, at TE = 5 and 8 ms (using a 3T MRI), were used to calculate magnetic field (B0) homogeneity. Tungsten and the thermoelectric cooler resulted in a significant B0 shift near the boundary of the phantom where the materials were placed, whereas the aluminum had the least effect on the field homogeneity. Based on a linear approximation, the material with the largest effect on the magnetic field, tungsten, would need to be placed approximately 0.6 cm outside of the RF receiver coil, making it 3.4 cm in total from the phantom to have negligible effect on the field homogeneity. There was negligible effect on magnetic field homogeneity when high or low voltage power were supplied to the CZT detector system.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".