Development of a novel, dual-modality image guidance system by combining a focused gamma probe with ultrasound imaging
Bibliographic record
Abstract
During breast cancer surgery, there are several modalities that a clinician can choose from to detect and localize cancerous tissue intraoperatively, with one of the most common being non-imaging gamma probes. However, deficiencies in the existing modalities make it difficult for clinicians to completely resect the tumour with clean margins, meaning the patient may have to undergo a revision surgery. We describe a novel, dual-modality image guidance system comprising a non-imaging, focussed gamma probe and an ultrasound transducer that simultaneously acquires molecular and anatomical data for a complete surgical guidance system. The custom-designed focussed gamma probe features a highly convergent collimator that achieves high-resolution in a remote focal region. Monte Carlo simulations show a 3.5 mm full width at half maximum resolution and a maximum sensitivity of approximately 7.8 cps/kBq for a 45 mm focal length collimator. The unique focal point design of the gamma probe enables the region with the highest resolution to be aligned within the ultrasound imaging plane for simultaneous, dual-modality acquisition. The first proof-of-concept computer-aided design for the hybrid imaging system is presented. Simultaneous acquisition was realized using a custom-designed holder that allows the focussed gamma probe and any commercially available ultrasound probe to be connected into a single, hand-held unit. The system also contains a translational stage that allows the focal point of the gamma probe to be adjusted axially within the ultrasound plane for optimal assessment. The development of this novel, dual-modality image guidance system should facilitate more accurate real-time, intraoperative tumour margin assessment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".