Quantifying Tumor Microvasculature With Optical Coherence Angiography and Intravoxel Incoherent Motion Diffusion MRI
Bibliographic record
Abstract
Intravoxel Incoherent Motion (IVIM) MRI is a contrast-agent-free microvascular imaging method finding increasing use in biomedicine. However, there is uncertainty in the ability of IVIM-MRI to quantify tissue microvasculature given MRI's limited spatial resolution (mm scale). Nine NRG mice were subcutaneously inoculated with human pancreatic cancer BxPC-3 cells transfected with DsRed, and MR-compatible plastic window chambers were surgically installed in the dorsal skinfold. Mice were imaged with speckle variance optical coherence tomography (OCT) and colour Doppler OCT, providing high resolution 3D measurements of the vascular volume density (VVD) and average Doppler phase shift ( $\overline {\Delta \phi }\text {)}$ respectively. IVIM imaging was performed on a 7T preclinical MRI scanner, to generate maps of the perfusion fraction f, the extravascular diffusion coefficient ${D}_{\textit {slow}}$ , and the intravascular diffusion coefficient ${D}_{\textit {fast}}$ . The IVIM parameter maps were coregistered with the optical datasets to enable direct spatial correlation. A significant positive correlation was noted between OCT's VVD and MR's f (Pearson correlation coefficient ${r}={0}.{34},{p}\lt {0}.{0001}\text {)}$ . Surprisingly, no significant correlation was found between $\overline {\Delta \phi }$ and ${D}_{\textit {fast}}$ . This may be due to larger errors in the determined ${D}_{\textit {fast}}$ values compared to f, as confirmed by Monte Carlo simulations. Several other inter- and intra-modality correlations were also quantified. Direct same-animal correlation of clinically applicable IVIM imaging with preclinical OCT microvascular imaging support the biomedical relevance of IVIM-MRI metrics, for example through f's relationship to the VVD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".