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Record W2738140003

ESTIMATING DIELECTRIC PROPERTIES OF BIOLOGICAL TISSUE

2016· article· en· W2738140003 on OpenAlexaffvenue
David C. Garrett, Bryce A. Besler, Jeremie Bourqui, Elise Fear

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPermittivityCalibrationDielectricMaterials scienceAntenna (radio)ConductivityRadarMicrowaveMicrowave imagingOpticsAcousticsElectronic engineeringComputer scienceOptoelectronicsPhysicsTelecommunicationsEngineering
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Microwave imaging has been of interest in recent decades, offering the potential of an affordable and non-ionizing medical diagnostic modality. This technique is sensitive to changes in dielectric properties such as permittivity and conductivity. One approach is microwave radar imaging, which creates images by focusing signals caused by reflections at material interfaces. In order to improve the images from radar approaches, patient-specific dielectric property estimations have been used to determine the speed of wave travel within the tissue [1]. Estimating dielectric properties of biological tissue can also be useful in emerging quantitative applications including bone health assessment. Methods such as local rod probes and antenna measurements of planar samples have been developed to estimate dielectric properties, but are of limited use for in vivo measurements. We have previously developed methods of permittivity estimation with a custom antenna, however this approach requires two measurements at different separation distances and is unable to estimate conductivity. This study aims to improve on methods of estimating permittivity and to add an estimate of conductivity of in vivo biological tissue by incorporating an antenna calibration method. METHODS In order to remove the influence of the antennas on measurements, a previously developed calibration method [2] was adapted to be used with a custom ultra-wideband antenna system [3], allowing permittivity and conductivity to be estimated over a range of frequencies. The two antennas are characterized as 2x2 matrices at each frequency, determined from two calibration measurements: the first is performed with the antennas separated by an electrical conductor, and the second measurement is done with the antennas in direct contact with one another. Measurements were performed using a vector network analyzer (Agilent, PNA-L, N5230A), and take less than 15 seconds. Measurement samples were placed between the two antennas, with their surfaces in contact with the entire antenna aperture. Dielectric properties were then estimated using the magnitude and phase of the calibrated transmission data. To validate this method, dielectric properties of several liquids were estimated and compared to literature values. RESULTS A general agreement was seen between the estimated and literature dielectric properties of several liquids, particularly for high permittivity materials. The estimated and literature permittivity of distilled water is shown in Figure 1. Several biological tissues were then measured such as human calf and heel, and porcine bone excisions. Literature values for these properties are limited as they are often done using local probes which only measure the properties at the surface of a sample. DISCUSSION AND CONCLUSIONS A calibration method has been adapted to enable an ultra-wideband antenna system to assess dielectric properties of in vivo tissue at microwave frequencies. The estimated properties of the tested liquids align closely to literature, providing confidence in estimates of biological tissues which have limited literature values. This technique can be used towards microwave radar signal speed estimates, and for quantitative property measurements. Future improvements could include a skin subtraction method to isolate the properties of bone or other tissue under the skin, and development towards microwave bone health assessment.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.322
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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