Simulation of Photoacoustic Imaging of Red Blood Cell Aggregation Using a Numerical Model of Pulsatile Blood Flow
Bibliographic record
Abstract
Photoacoustic (PA) imaging of blood flow can provide label free and non-invasive assessment of red blood cell (RBC) aggregation and the oxygen saturation (sO2). Our group has previously demonstrated that the interrelationship between RBC aggregation and the sO2during a pulsatile blood flow could be potentially assessed using PA imaging. The pulsatile blood flow yields spatiotemporal changes in RBC aggregation, affecting PA imaging. A simple particle motion model was developed based on the blood flow velocity measured from the human radial artery (RA). The positions of randomly distributed, identical circular particles (2.7 μm radius) in the lateral-axial plane (20 mm by 2 mm) were traced at each time step of an experimentally measured velocity profile. At each step, the time dependent PA power (PPA) from each single cell (or particles interacting to form aggregates) was computed by modeling and accounting for the directivity of a 21 MHz (9.2 to 32.8 MHz bandwidth) linear array. In-vivo PA images of the RA of healthy volunteers were acquired using the VevoLAZR equipped with a 21 MHz linear-array probe. The measured PA images were compared to the simulated PA images. The aggregates formed a parabolic front along the axial direction and were driven to the right-hand side along the lateral direction as the simulation propagated in time. The PPAwas also large at the parabolic front, and was also driven to the right-hand side for every time step. The spatiotemporal distribution of the computed PPAwas comparable to the experimental PPA. Specifically, the PPAincreased by 12 dB along the lateral direction. These results can be used to study the label-free, non-invasive assessment of the spatiotemporal distribution of sO2in vivo. Furthermore, the improved particle model can provide insights into the mechanism of PA wave generation from RBC aggregation during in vivo blood flow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".