A strategy to prevent a temperature‐induced MRI artifact in warm liquid phantoms due to convection currents
Bibliographic record
Abstract
MRI phantom studies often fail to mimic the temperature of the human body, which can negatively impact accuracy. An artifact induced by increasing temperature in liquid phantoms was observed, presenting a significant challenge to temperature‐controlled experiments. In this study we characterize and provide a solution to eliminate this temperature‐induced MRI artifact. Low concentration (0.5–2.5 mM) agar phantoms were prepared. Utilizing a temperature‐controlled phantom holder, T1‐ and T2‐weighted structural images were acquired at 7 T along with quantitative B0, B1, T1, T2 and ADC maps at both 25 and 37°C. Additionally, computer simulations were conducted to demonstrate the fluid flow and thermal flux patterns in water to provide an insight into the origins of the artifact. Evidence from computer simulation and quantitative MRI strongly suggest the artifact was caused by heat transfer in the form of natural convection leading to structured patterns of signal loss in MR images. The artifact was present up to agar concentrations of 1.5 mM (T1 = 3068 ± 16 ms, T2 = 1052 ± 20 ms, ADC = 2.29 ± 0.36 × 10−3 mm2/s at 25°C; T1 = 3928 ± 44 ms, T2 = 1122 ± 24 ms, ADC = 2.64 ± 0.49 × 10−3 mm2/s at 37°C), above which point increased sample viscosity no longer allows for convection currents, thereby eliminating the artifact. The methodology described in this work simplifies quantitative MR acquisition of liquid phantoms at physiological temperature by suppressing convection currents with relatively small changes to intrinsic MR parameters (T1 increased by 1.4% and T2 decreased by 17% for 1.5 mM agar at 25°C).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".