Rheology and heat transport properties of a hydroxyethyl cellulose-based MRI tissue phantom
Bibliographic record
Abstract
Abstract A saline solution of hydroxyethyl cellulose has been recommended for use as a tissue phantom in testing the behavior of medical devices in MRI scanners. It has been stated in the standards governing these tests that the viscosity of the fluid used should be large enough that bulk transport or convection currents are not supported. In this study we evaluated a hydroxyethyl cellulose phantom based on an ASTM standard to determine the degree to which it supports convective, as compared to conductive, heat transport. We study the rheological properties of this fluid, and find that it behaves as a typical viscoelastic polymer solution. As a result, it flows in response to local heating, such as would occur due to eddy-current heating of a metallic device in an MR scanner. We use laboratory experiments and numerical simulations to determine the convective and conductive contributions to the heat transport in a simple model of this system. Our results indicate that convective heat transport is of the same order of magnitude as conductive transport under conditions typical of MRI device tests. This indicates that heating tests conducted with this fluid are not completely conservative in terms of estimating local temperature changes for medical devices in vivo . It also indicates that convective processes should be included along with conduction in computer simulations of device heating in order to allow accurate comparison with experimental measurements.
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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 | 0.000 |
| 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".