Three‐dimensional reconstruction of a vascular network by dynamic tracking of magnetite nanoparticles
Bibliographic record
Abstract
PURPOSE: Visualization of small blood vessels feeding tumor sites provides important information on the tumors and their microenvironment. This information plays an important role in targeted drug therapies using magnetic gradients. However, capabilities of current clinical imaging modalities may be insufficient to resolve complex microvascular networks. The purpose of this study is to map the vascular network, 3D, based on the magnetic susceptibility contrast. METHODS: Magnetic particles induce an inhomogeneity in the MRI's magnetic field in an order much larger than their real size. This is an approach to compensate the spatial resolution insufficiency of a clinical MR scanner. Micron-sized agglomerations of magnetite nanoparticles were injected in a 3D phantom vascular network, and a fast multislice, multiacquisition MR sequence was applied to track the agglomerations along their trajectories. The experiment was performed twice for two different imaging planes: coronal and transversal. The susceptibility artifact in the images indicated the presence and the position of the agglomerations. The calculated positions through multiple images were assembled to build up the 3D distribution of the vascular network. RESULTS: The calculated points were compared with the centerline of the channels, extracted from the 3D reference image, to determine the absolute measurement error. The mean error was measured to be approximately half of the pixel's size. It was found that the positioning error on the axis perpendicular to the imaging slice was nearly twice as high as on the imaging plane axes due to the slice thickness. In order to compensate for the lack of resolution on the perpendicular axis, the reconstruction was performed using a combination of coronal and transversal data. The combination of the coordinates led to a significant decrease in the mean measurement error at each segment in the vascular network (p < 0.001). CONCLUSIONS: A method for 3D reconstruction of a microvascular network based on the susceptibility contrast in MRI and using a clinical scanner and a commercial receiver coil was proposed. The method presents a novel approach for reconstruction of vascular networks using the susceptibility effect. The proposed method may be applied to resolve vascular networks at a micrometric scale.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".