Elimination of imaging artifacts in second harmonic generation microscopy using interferometry
Bibliographic record
Abstract
Conventional second harmonic generation (SHG) microscopy might not clearly reveal the structure of complex samples if the interference between all scatterers in the focal volume results in artefactual patterns. We report here the use of interferometric second harmonic generation (I-SHG) microscopy to efficiently remove these artifacts from SHG images. Interfaces between two regions of opposite polarity are considered because they are known to produce imaging artifacts in muscle for instance. As a model system, such interfaces are first studied in periodically-poled lithium niobate (PPLN), where an artefactual incoherent SH signal is obtained because of irregularities at the interfaces, that overshadow the sought-after coherent contribution. Using I-SHG allows to remove the incoherent part completely without any spatial filtering. Second, I-SHG is also proven to resolve the double-band pattern expected in muscle where standard SHG exhibits in some regions artefactual single-band patterns. In addition to removing the artifacts at the interfaces between antiparallel domains in both structures (PPLN and muscle), I-SHG also increases their visibility by up to a factor of 5. This demonstrates that I-SHG is a powerful technique to image biological samples at enhanced contrast while suppressing artifacts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".