A Tracked High-Resolution Ultrasound Endoscope for Minimally Invasive Brain Surgery
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We have developed a novel intraoperative imaging adjunct to address limitations in neurosurgical guidance technology: a miniature, high-resolution, tracked ultrasound (US) imaging endoscope. This report describes the fabrication and preliminary testing of the device, which aims to enhance neurosurgery by providing precise, real-time visualization of the brain within narrow surgical pathways. METHODS: The work has 2 components: (1) the development and clinical validation of the US endoscope and (2) its integration with a surgical navigation system (SNS), with tracking validated in a brain phantom. A miniature (3.8 × 4 mm cross-section) 30 MHz phased array with 64 elements was developed, and probe performance and safety were tested. Investigational testing of the untracked imaging probe was performed in 3 adult neurosurgery cases (2 glioblastoma resections and 1 aneurysm clipping). B-mode and Doppler imaging data were collected to explore the probe's ability to visualize anatomy intraoperatively. The probe was integrated with a commercial SNS and tested in a brain phantom containing a simulated tumor and landmark. Agreement between the landmark location based on B-mode US and navigated 3-dimensional T1-weighted MRI was characterized. RESULTS: The probe demonstrated axial and lateral resolutions of 38 and 113 µm, respectively. During tumor resection, it differentiated tumor from healthy tissue and identified residual tumor missed by conventional surgical guidance. Key structures were also visualized during aneurysm surgery. The US probe was successfully integrated with a SNS. When this combined system was tested in a brain phantom, there was good agreement between tumor boundaries in navigated MRI and US images, with the apparent landmark location agreeing within µ = 2.90 mm over 25 trials. CONCLUSION: This first-of-its-kind imaging system shows potential as a novel adjunct for neurosurgical guidance. Further clinical testing is needed to fully validate its efficacy.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".