Development of a Hyperspectral NIRS System for Functional Imaging of the Brain and for Cerebral Status Monitoring During Cardiac Surgery
Bibliographic record
Abstract
Hyperspectral near infrared spectroscopy (hNIRS) is a noninvasive, real-time imaging modality with an improved quantitative accuracy and increased number of detectable chromophores. It uses the broadband spectrum of light wavelengths in the range of 700 – 1100 nm and is based on the unique absorbance property of molecules and the fact that all biological tissues are relatively transparent to these wavelengths which allow for measuring concentrations of light absorbing molecules such as the Oxy- and Deoxy- hemoglobin and Cytochrome C Oxidase. As opposed to fMRI, PET and SPECT, hNIRS is inexpensive and portable. The purpose of this thesis project was to employ advantages of hNIRS by developing the multichannel hNIRS set-up for the simultaneous assessment of multiple areas of the brain and to test the system in clinical applications. To achieve these goals, I developed a new optical fiber bundle design providing improvement of the optical power throughput into the hNIRS light detectors. I also developed a novel probe for measurements on hairy areas of the human head. To validate the hNIRS system I used it simultaneously with fMRI, which revealed a good correlation of hNIRS and fMRI BOLD signals from the brain. The multichannel hNIRS set up with the increased signals due to the novel optical fiber bundles was then used during various brain activation protocols, which in the future can allow for the assessment of patients with mild traumatic brain injuries (mTBI). Finally, the hNIRS system with the new fiber bundles was compared with a commercial NIRS system in clinical setting for brain monitoring of patients during the transcatheter aortic valve implantation operation (TAVI).
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".