Microwave Antenna Radiometric Temperature Sensing System for Non-Invasive Deep Tissue Thermal Analysis
Bibliographic record
Abstract
This paper investigates the optimal conditions for temperature modeling in deep human tissue, with a focus on non-invasive tumor detection. A custom rectangular microwave patch antenna and an integrated radiometer system are designed and fabricated. The study emphasizes the determination of the optimal resonance frequency and directivity/radiation patterns, employing characteristic modes theory for analysis. This study integrates the antenna developed with realistic muscle phantoms, engineered to replicate human tissue properties, enabling accurate simulations of microwave interactions. Analysis of S − parameters and impedance characteristics is conducted to evaluate performance. A radiometer, adapted from astrophysical instrumentation principles, is utilized to improve temperature measurement precision, with key performance metrics assessed for subsequent optimization. Integration of an antenna, phantom models, and a radiometer system enhances diagnostic accuracy and sensitivity, presenting a promising tool for advanced clinical applications. The antenna-radiometer system enables modeling of temperature distribution at a 30 mm depth within a phantom, with potential error effects in temperature estimation analyzed to ensure reliability. Validation is achieved using fabricated phantoms engineered to replicate human tissue properties. Experimental results from muscle phantoms substantiate the system’s efficacy and performance. However, discrepancies in measured outcomes suggest errors, which are systematically investigated and discussed. The study concludes by assessing the technology’s potential to advance medical imaging, particularly for early tumor detection and monitoring, and outlines future research directions to optimize this approach for clinical deployment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".