SU‐E‐I‐77: A Phantom to Assess EIT/CT Imaging System
Bibliographic record
Abstract
Purpose: Fusion of Electrical Impedance Tomography (EIT) and Computed Tomography (CT) can potentially provide functional EIT information along with high resolution anatomical information from CT. In this study, we have developed a phantom for evaluating an EIT/CT imaging system. Methods: An electrically conductive phantom was prepared by pouring a molten mixture of gelatin solution and glycerol into a cylindrical container. The mixture was then allowed to solidify. A cylindrical cavity was then created at the center of the medium. Sixteen electrodes were attached equi‐distantly around the phantom. A fixed current was injected through a pair of electrodes, and the induced boundary voltages across all pairs of neighboring electrodes were measured sequentially. The entire voltage measurement process was repeated for all combinations of electrodes through which the current was injected. EIT images were obtained from the boundary voltage data using EIDORS software. The CT data was acquired using a Phillips CT Scanner. EIT and CT data were also collected when the central cavity of the medium was filled with de‐ionized water and 0.9% saline separately. The two images were combined using the boundary and statistical information, extracted using the morphological image processing modules of Matlab. Image quality was evaluated by measuring the entropy of the CT, EIT and the fused images. Results: Phantom regions of saline and distilled water show distinct contrast as compared to CT which fails to differentiate the regions. The location of the contrast objects was found to match their expected locations with respect to CT. Conclusion: We have developed a phantom to assess EIT/CT image quality. The phantom can be extended to include measures such as signal to noise, contrast to noise, relative information content and geometric accuracy of an EIT/CT imaging system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".