Electrical Impedance Tomography in 3D: Characterization and Evaluation
Bibliographic record
Abstract
Electrical Impedance Tomography (EIT) uses body surface electrical stimulation and measurements to create images of the conductivity contrasting fluids.Identifying and monitoring regions of inhomogeneous behaviour in the lungs can be a vital part of the treatment of lung injuries.Traditionally, EIT uses a placement of electrodes in a 2D ring around the thorax, and thus produces cross-sectional images.3D EIT has a potential to image regional inhomogeneities with 3D spatial extent and to improve planar resolution over 2D EIT.However, while 3D reconstruction algorithms are available, little evaluation has been done to understand the performance of 3D EIT in terms of the measurement configurations available.This thesis focuses on this evaluation task.It has three main objectives.First, to examine different measurement patterns, and determine the best-suited pattern for in vivo 3D lung imaging of regional inhomogeneities.Second, to capture regional inhomogeneities in the lungs caused by gravitational effects by recording EIT on human subjects in the standing, sitting, supping, and decline postures.This method is used in order to cause and measure regional inhomogeneities in the lungs.Third, to verify the ability of 3D EIT to measure global lung volume changes in volunteers by comparing reconstructed images to spirometry lung volume measurements.To address these objectives, the thesis develops an analysis methodology, and applies it to data from simulation, phantom, and measurements on 8 healthy volunteers.Results indicate that 3D EIT can provide meaningful and stable reading of the global change in lung volume.Functional EIT images are created from inhalation curve features to analyze the effect of posture on regional lung behaviour with the greater lung activity moving upwards in the thorax when moving from the standing postures to a declined posture.The Planar and Zigzag Offset patterns show statistically significant difference in vertical lung activity between the decline and standing postures and the supine and standing postures for several fEIT features.While inconsistent, the positive results indicate that 3D EIT is capable of capturing region lung inhomogeneities in 3D space.Overall, this thesis presents analysis methodologies (simulation, phantom, and experimental) to characterize and optimize 3D EIT imaging.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".