Contrast-enhanced 3D tracking of robotic capsules in ultrasound using a dynamic acoustic retroreflector
Bibliographic record
Abstract
Ingestible robotic capsules are a minimally-invasive option for diagnosing and treating conditions of the gastrointestinal tract. Ultrasound has the potential to provide the accurate and real-time image guidance that is necessary for targeted procedures; however, existing ultrasound-based methods have limited clinical utility due to factors such as low capsule contrast, high background noise, unsuitable capsule motion requirements, low frame rates, and reliance on raw ultrasound radiofrequency data, which is rarely accessible in clinical systems. This work presents a high-contrast ultrasound tracking target that can be equipped on a robotic capsule, and that emits a periodic flashing signal in clinically-available B-mode images. The tracking target is a shape-changing acoustic retroreflector whose retroreflection can be constructed and destroyed by changing the device's configuration through the application and removal of a low magnetic field (16 mT). Subsequent 3D spatial localization of the periodic intensity signal in B-mode is accomplished with an efficient Fourier-based network. Tracking of a stationary dummy capsule was evaluated in an ex-vivo porcine stomach phantom, achieving a mean position error of 0.71 mm and a detection accuracy of 97.4%. Tracking update rates reached up to 130 Hz. Capsule torque-based rolling locomotion was achieved by exploiting the net magnetic moment produced by two on-board retroreflectors. Our tracking system is compatible with B-mode, a moving probe, and stationary and moving capsules, demonstrating potential for clinical use in the ultrasound-guided localization of robotic capsules.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".