Simulation of Capillary Hemodynamics and Comparison with Experimental Results of Microphantom Perfusion Weighted Imaging
Bibliographic record
Abstract
BACKGROUND: Perfusion imaging, one of MRI's techniques, is widely used to test damaged tissues of the body. The parameters used in this technique include cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). The MRI scanner contains a device called a "phantom", which controls the accuracy of various imaging models. OBJECTIVE: Our goal is to design and produce a microphantom to control the perfusion-imaging model in MRI scanners. MATERIAL AND METHODS: Firstly, in an analytical study type, we designed the phantom based on Murray's minimum work rule using AutoCAD software. Next, the phantom was fabricated using lithography and then imaged using a Siemens Magnetom 3T Prisma MRI scanner at the National Brain Laboratory. Finally, the velocity and pressure in the capillary network was simulated using COMSOL software. RESULTS: CBF, CBV, and MTT curves for the capillary network were obtained at different times. In addition, the simulations showed that the velocity and pressure in the capillary network were between 0.0001 and 0.0005 m/s and between 5 and 25 mm/Hg, respectively. CONCLUSION: The fabricated microphantom was used to simulate the movement of blood within tissues of the body. Different parameters of perfusion imaging were measured inside the phantom, and they in the phantom were similar to in the body.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".