Tissue Imaging Technique Using Near-Infrared Illumination of Whispering Gallery Mode Silicon-Based Resonator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This letter introduces a novel technique for achieving high-precision 2-D tissue imaging by exploiting the sensitivity of a whispering gallery mode (WGM) silicon resonator’s conductivity to near-infrared (NIR) illumination. The WGM silicon resonator, in conjunction with a microstrip line, acts as the primary sensing element. To ensure precise imaging, the tissue under test (TUT) specimen is meticulously positioned on the resonator at a specific distance and manipulated using a 2-D scanner with 3-mm steps. By directing NIR light emitted from a light-emitting diode (LED) through the scanning TUT sample onto the WGM resonator, variations in the silicon resonator’s conductivity are harnessed, resulting in changes in the magnitude of the transmission coefficient (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula>). The alteration in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> during scanning is contingent upon the absorption of NIR through TUT. As the TUT undergoes scanning, the measured transmission coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> parameters are transformed into a 2-D image map. This method effectively discriminates between fat and muscle tissues, underscoring the feasibility and practicality of this approach. Importantly, the proposed methodology shows promise for detecting various biosensors and holds potential applications in breast cancer detection.
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.
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 | 0.001 |
| 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 it