Temperature Mapping using Mid-Field Magnetic Resonance Imaging
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) is a non-invasive imaging modality with excellent soft tissue contrast and sensitivity to tissue temperature. MRI use is growing in Canada with expectation that this is expected to continue in the medium term, with more wide adoption of MRI and in particular a renewed focus on MR systems which deviate from the most commonly used 1.5T field strength system. By implementing systems which do not use as strong magnets and instead operate\nGenerally, as the field strength of an MR system decreases, the signal received when imaging also decreases, which makes it difficult to implement some applications which are standard at higher field. One such application is temperature mapping on a these <1T >systems, which can be used to monitor thermal therapies interventionally.\nThis thesis addresses the potentials for implementing temperature mapping at 0.5T, both in the creation of a tissue mimicking phantom which can be used to compare temperature mapping methods and implementing temperature maps both in vivo and in the custom phantom. As well, motivated by the sensitivity that thermal mapping has to external disturbances, the challenges that these accessible MR systems face when being in non-specialized environments is addressed, as this can potentially limit the efficacy of temperature mapping.\nThis work ultimately demonstrates the acceptable capabilities of a 0.5T system to map temperatures with an adequate temporal resolution, along with presenting practical solutions to operating a system in non-traditional locations.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".