Electrical Impedance Tomography for Robot-Aided Internal Radiation Therapy
Bibliographic record
Abstract
High dose rate brachytherapy (HDR) is an internal based radiation treatment for prostate cancer. The treatment can deliver radiation to the site of dominant tumor growth within the prostate. Imaging methods to delineate the dominant tumor are imperative to ensure the maximum success of HDR. This paper investigates the feasibility of using electrical impedance tomography (EIT) as the main imaging modality during robot-aided internal radiation therapy. A procedure utilizing brachytherapy needles in order to perform EIT for the purpose of robot-aided prostate cancer imaging is proposed. It is known that cancerous tissue exhibits different conductivity than healthy tissue. Using this information, it is hypothesized that a conductivity map of the tissue can be used to locate and delineate cancerous nodules via EIT. Multiple experiments were conducted using eight brachytherapy needle electrodes. Observations indicate that the imaging procedure is able to observe differences in tissue conductivity in a setting that approximates transperineal HDR and confirm that brachytherapy needles can be used as electrodes for this purpose. The needles can access the tissue at a specific depth that traditional EIT surface electrodes cannot. The results indicate the feasibility of using brachytherapy needles for EIT for the purpose internal radiation therapy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".