Impact of histology region size on measured dielectric properties of biological tissues
Bibliographic record
Abstract
Accurate knowledge of the dielectric properties of biological tissues is necessary for the design and development of electromagnetic medical technologies. Both electromagnetic diagnostic and therapeutic techniques depend heavily on the dielectric properties of the tissues in the region of interest. These properties quantify the accuracy and efficacy of systems, and enable realistic modelling and simulation prior to clinical evaluation. Despite these strong needs, the dielectric properties reported in the literature have suffered from significant inconsistencies. These inconsistencies have mainly been attributed to clinical confounders that have not historically been well-controlled. In this work, the sensing depth of the dielectric probe, a key clinical confounder, is investigated using heterogenous biological samples composed of porcine muscle and fat. Complex heterogeneous samples can contain several different types of tissues, which are identified through histology. When measuring the dielectric properties, it is crucial to know which tissues contribute to the measurements. In order to achieve this, a histology region is used, which enables correspondence between complex tissue samples and the measured dielectric properties. The histology region is given by the sensing depth in the longitudinal direction, and the sensing radius in the radial direction. We perform dielectric measurements on heterogenous samples and calculate the sensing depth of the dielectric probe for this measurement scenario. We then examine how errors in the assumed sensing depth value affect quantification of the tissue composition. This study demonstrates that the sensing depth, and thus the histology region, has a significant impact on how we interpret the dielectric properties of a sample, indicating that this region must be defined and measured with extreme care. With an improved understanding of these parameters, more accurate and repeatable dielectric measurements will be possible, thus facilitating the development of electromagnetic medical devices.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".