First experience with spatial frequency domain imaging and red-lightexcitation of protoporphyrin IX fluorescence during tumorresection
Bibliographic record
Abstract
Fluorescence-guided surgery (FGS) enhances intraoperative visualization of tumors to maximize safe resection, and quantitative fluorescence imaging (qFI) of protoporphyrin IX (PpIX) has provided additional information for guidance during intracranial tumor surgery. Previous developments in fluorescence quantification have demonstrated that the depth of fluorescence signals can be estimated given known optical properties in a lab setting, and now with the work described here that these optical properties can be determined in vivo in human brain tissue in the operating room (OR) during tumor resection procedures. More specifically, we report the first depth estimation of subsurface tumor intraoperatively, achieved with the combination of spatial frequency domain imaging (SFDI) for optical property measurement and red-light excitation of PpIX. We modified a commercial surgical microscope (Zeiss) with a digital light processing module (DLI Austin, TX) to modulate light from a xenon arc lamp to illuminate the field. White-light excitation and a liquid crystal tunable filter (LCTF Verispec) were used to measure diffuse reflectance at discrete wavelengths of 670 nm and 710 nm on a sCMOS camera. An illumination-side filter wheel allowed excitation of PpIX fluorescence at 405 nm and 635 nm, and the LCTF measured fluorescence emissions at 670 nm and 710 nm. Data acquisition and processing generated wide-field images of the depth of PpIX fluorescence within 1 minute in the OR. The ability of the clinical microscope to perform optical property mapping with SFDI and convert these wide-field estimates into images of the depth of fluorescence was tested in tissue simulating phantoms and in vivo during a craniotomy for brain tumor resection. Results indicate that wide-field optical property estimates with SFDI can be combined with depth sensing algorithms to produce maps of the depth of PpIX when exposed to red-light in the OR.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".