Real‐time guidance of thermal and ultrashort pulsed laser ablation in hard tissue using inline coherent imaging
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: During tissue ablation, laser light can be delivered with high precision in the transverse dimensions but final incision depth can be difficult to control. We monitor incision depth as it progresses, providing feedback to ensure that material removal occurs within a localized target volume, reducing the possibility of undesirable damage to tissues below the incision. MATERIALS AND METHODS: Ex vivo cortical and cancellous bone was ablated using pulsed lasers with center wavelengths of 1,064 and 1,070 nm, while being imaged in real-time using inline coherent imaging (ICI) at rates of up to 300 kHz and axial resolution of ∼6 µm. With real-time feedback, laser exposure was terminated before perforating into natural inclusions of the cancellous bone and verified by brightfield microscopy of the crater cross-sections accessed via side-polishing. RESULTS: ICI provides direct information about incision penetration even in the presence of intense backscatter from the pulsed laser and plasma emissions. In this study, ICI is able to anticipate structures 176 ± 8 µm below the ablation front with signal intensity 9 ± 2 dB above the noise floor. As a result, the operator is able to terminate exposure of the laser sparing a 50 µm thick layer of bone between the bottom of the incision to a natural inclusion in the cancellous bone. Versatility of the ICI system was demonstrated over a wide range of light-tissue interactions from thermal regime to direct solid-plasma transition. CONCLUSIONS: ICI can be used as non-contact real-time feedback to monitor the depth of an incision created by laser ablation, especially in heterogeneous tissue where ablation rate is less predictable. Furthermore, ICI can image below the ablation front making it possible to stop laser exposure to limit unintentional damage to subsurface structures such as blood vessels or nervous tissue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".