EPI proton resonant frequency temperature mapping at 0.5T in the brain: Comparison to single‐echo gradient recalled echo
Bibliographic record
Abstract
Abstract Purpose Evaluate the use of both single‐echo gradient recalled echo (SE‐GRE) and EPI approaches to creating temperature maps on a mid‐field head‐only scanner, both in vivo and on a tissue mimicking gel. Methods Three 2D protocols were investigated (an SE‐GRE, single‐shot EPI, and an averaged single‐shot EPI). The protocols used either a gradient recalled acquisition or an echo planar acquisition, with EPI parameters optimized for the longer at lower field‐strengths. Phantom experiments were conducted to evaluate temperature tracking while cooling, comparing protocol to measurements from an optical fiber thermometer. Studies were performed on a 0.5T head only MR scanner. Temperature stability maps were produced in vivo for the various protocols to evaluate precision. Results The use of an EPI protocol for thermometry improved temperature precision in a temperature control phantom and provided an 18% improvement in temperature measurement precision in vivo. Temperature tracking using a fast (<2 s) update rate EPI thermometry sequence provided a similar precision to the slower SE‐GRE protocol. Conclusion While SE‐GRE PRF thermometry shows good performance, EPI methods offer improved tracking precision or update rate, making them a better option for thermometry in the brain at mid‐field.
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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 | 0.002 |
| 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 | 0.001 |
| 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".