Preclinical Imaging and Spectroscopy in the NIR-II Window with Indocyanine Green (ICG) and Single-Walled Carbon Nanotubes
Bibliographic record
Abstract
We developed a near-infrared small animal imaging system, IR VIVO, that provides real-time images, videos and spectral imaging in the NIR-I and shortwave infrared (SWIR or NIR-II) regions. We found that SWIR wavelengths can give optimal resolution for in vivo optical imaging of deep organs in mice up to 3 cm due to the low tissue autofluorescence, scattering and absorption of light at these wavelengths. We also showed how this higher penetration depth can enable the detection of small wavelength changes in the emission of carbon nanotube-based sensors implanted in vivo using a continuously tunable filter. First, we demonstrated superior image clarity and penetration depth at NIR-II wavelengths (1000-1700 nm) in vivo using an FDA approved dye, indocyanine green (ICG) in a mouse. We used 780 nm excitation, detection using a Zephir 1.7 InGaAs camera and 1250 nm long-pass emission filter. We found low tissue autofluorescence at 1250 nm. The ICG dye imaging resulted in superior visualization of microvasculature, perfusion, measurement of hearst rate, respiratory rate, hepatobiliary and intestinal contractions. Further anatomical and functional imaging was carried out by looking at the kinetics of ICG, resulting in the identification of different organs. Second, In vivo near-infrared hyperspectral imaging of carbon nanotubes was conducted using a continuously-tunable filter in the imager. A diffraction volume Bragg grating (VBG), was used to provide narrow-band wavelength selection. Coupled with a homogeneous global illumination, spectrally-defined images were acquired in the entire field of view. The result was a dataset containing both 2D spatial information and the full spectrum for each point in the image. We conducted near-infrared hyperspectral imaging of single-walled carbon nanotubes to measure small wavelength changes of the nanotubes implanted into live mice. Hyperspectral measurements of carbon nanotube sensors for lipids in the liver as well as implantable sensors for doxorubicin were conducted, facilitating liver disease monitoring and drug pharmacokinetics measurements. Finally, we believe wide-area NIR-II imaging and spectral/hyperspectral measurements have broad potential applications for the use of carbon nanotubes and other NIR-I/II materials in basic materials/biology, translational, and clinical work, including sensor arrays, point-of-care measurements, implants, whole small animal imaging and intraoperative/surgical imaging.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".