Magnetic resonance thermometry of flowing blood
Bibliographic record
Abstract
Blood temperature is a key determinant of tissue temperature and can be altered under normal physiological states, such as exercise, in diseases such as stroke or iatrogenically in therapies which modulate tissue temperature, such as therapeutic hypothermia. Currently available methods for the measurement of arterial and venous temperatures are invasive and, for small animal models, are impractical. Here, we present a methodology for the measurement of intravascular and tissue temperature by magnetic resonance imaging (MRI) using the lanthanide agent TmDOTMA − (DOTMA, tetramethyl‐1,4,7,10‐tetraazacyclododecane‐1,4,7,10‐tetraacetic acid; Tm, thulium). The approach makes use of phase‐sensitive imaging measurements, combined with spectrally selective excitation, to monitor the temperature‐dependent shift in the resonance of proton nuclei associated with water and with methyl groups of TmDOTMA − . Measurements were first made in a flow phantom modelling diastolic blood flow in the mouse aorta or inferior vena cava (IVC) and imaged using 7‐T preclinical MRI with a custom‐built surface coil. Flowing and static fluid temperatures agreed to within 0.12°C for these experiments. Proof‐of‐concept experiments were also performed on three healthy adult mice, demonstrating temperature measurements in the aorta, IVC and kidney following a bolus injection of contrast agent. A small (0.7–1°C), but statistically significant, higher kidney temperature compared with the aorta ( p = 0.002–0.007) and IVC ( p = 0.003–0.03) was shown in all animals. These findings demonstrate the feasibility of the technique for in vivo applications and illustrate how the technique could be used to explore the relationship between blood and tissue temperature for a wide range of applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".