Estimation and Use of Prior Information in FEM-CSI for Biomedical Microwave Tomography
Bibliographic record
Abstract
Prior information is used to improve imaging results obtained using the finite-element contrast source inversion ( FEM-CSI ) of a microwave tomography (MWT) dataset collected as part of a forearm imaging study. The data consist of field measurements taken inside a prototype MWT system that uses simple dipole antennas and a saltwater matching medium. Initial images of the 2-D cross-sectional dielectric profile of the individuals' arms are reconstructed using FEM-CSI. These initial “blind” imaging results show that the image quality is dependent on the thickness of the arm's peripheral adipose tissue layer: Thicker layers of adipose tissue lead to poorer overall image quality. The poor image quality for arms with high levels of adipose tissue is not improved by changing the matching fluid's complex dielectric constant. Introducing prior information into the FEM-CSI algorithm in the form of an inhomogeneous background consisting of an adipose layer surrounding a muscle region provides substantial improvement of the image quality: The internal anatomical features of the arm are resolved for each of the five datasets. Two methods are employed to estimate the arm periphery and adipose layer thickness from the blind imaging results: manual estimation and a novel image segmentation algorithm based on global optimization using simulated annealing.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".