Steady state temperature rise in multilayered tissue due to arbitrary periodic SAR using finite difference FFT and transfer function method
Why this work is in the frame
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Bibliographic record
Abstract
Steady state (SS) and transient temperature-rise in tissue from radiofrequency exposure forms the underlying basis for limits in international exposure guidelines. Periodically pulsed or intermittent exposures form a special case of having both peak and average levels, producing temperature-rise oscillations in the SS. Presented here is a method for determining tissue temperature-rise for periodic specific absorption rate (SAR) modulation having arbitrary waveform. It involves the finite difference solution of a form of the Pennes Bioheat Transfer equation (BHTE) and uses the concept of the transfer function and the Fast Fourier Transform (FFT). The time-dependent BHTE is converted to a SS harmonic version by assuming that the time-dependent SAR waveform and tissue temperature can both be represented by Fourier series. The transfer function is obtained from solutions of the harmonic BHTE for an assumed SAR waveform consisting of periodic impulses. The temperature versus time response for an arbitrary periodic SAR waveform is obtained from the inverse FFT of the product of the transfer function and the FFT of the actual SAR waveform. This method takes advantage of existing FFT algorithms on most computational platforms and the ability to store the transfer function for later re-use. The transfer function varies slowly with harmonic number, allowing interpolation and extrapolation to reduce the computational effort. The method is highly efficient for the case where repeated temperature-rise calculations for parameter variations in the SAR waveform are sought. Examples are given for a narrow, circularly symmetric beam incident on a planar skin/fat/muscle model with rectangular, triangular and cosine-pulsed SAR modulation waveforms. Calculations of temperature-rise crest factor as a function of rectangular pulse duty factor and pulse repetition frequency for the same exposure/tissue model are also presented as an example of the versatility of the method.
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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 it