THE EFFECT OF ANTENNA INCIDENT FIELD DISTRIBUTION ON MICROWAVE TOMOGRAPHY RECONSTRUCTION
Bibliographic record
Abstract
For microwave tomography applications, we show that the utilized incident field distribution can affect the achievable image quantitative accuracy and resolution.In particular, for the synthetic cases considered here, it is shown that the use of a focused incident field distribution within the imaging domain often results in either enhanced or equivalent image reconstruction as compared to the use of an omnidirectional incident field distribution. INTRODUCTIONIn microwave tomography (MWT), the dielectric profile of the object of interest (OI) is to be found using microwave scattering measurements collected outside the object.To this end, the OI is successively illuminated by a number of antennas located around the OI.The scattering data emanating from the OI is then used by an appropriate nonlinear inversion algorithm so as to generate quantitative images from the dielectric profile of the OI.To make MWT a viable imaging method, specially for clinical applications, its current achievable image quantitative accuracy and resolution need to be enhanced.Broadly speaking, two general approaches have been suggested to increase the image quantitative accuracy and resolution achievable from MWT: (1) collecting more scattering information, and (2) interpreting the collected scattering information in a better way.Within the framework of these two approaches, some of the utilized techniques are (i ) increasing the number of antennas (or, probes) [1], (ii ) using multiple-frequency data sets [2], (iii ) using different boundary conditions [3], (iv ) using an appropriate Green's function [4], (v ) simultaneous use of transverse magnetic and electric data sets [5], (vi ) using appropriate data calibration techniques [6], (vii ) using more effective inversion algorithms and regularization techniques [7,8], (viii ) accurate MWT system modeling [9], (ix ) using a priori information [10], etc.Herein, we investigate whether the utilized incident field distribution in MWT, defined as the field distribution within the imaging chamber in the absence of the OI, can affect the achievable image accuracy and resolution.This incident field distribution is usually governed by the near-field distribution of the utilized transmitting antenna as MWT antenna systems usually operate in their near-field zones.It should also be noted that the effect of antenna near-field distribution on the achievable image from microwave radar-based imaging has been studied in [11]; however, to the best of our knowledge, it has not yet been investigated for MWT.(A preliminary investigation of this topic has been presented by the authors as a one-page abstract in [12].)In what follows, we first explain why the choice of the incident field distribution can affect the achievable MWT image accuracy.The numerical model adopted for the incident field distribution will then be described.We will then numerically show how an appropriate choice of the incident field distribution can lead to enhanced image reconstruction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".