Consequences of the laser speckle imaging computation method on analysis of renal autoregulation dynamics
Bibliographic record
Abstract
Laser speckle imaging generates a surface perfusion map by analyzing speckle contrast pattern statistics over either a spatial or temporal set of pixels. Use of a spatial set reduces spatial but increases temporal resolution, while a temporal set increases spatial but decreases temporal resolution. We have found, for renal autoregulation frequencies, signals from single pixels have low signal‐noise ratio (SNR), but averaging pixels in each frame increases the SNR. We compared the moorFLPI (Moor Instruments, UK) imager choice of spatial or temporal statistics for analyzing renal autoregulation in terms of: 1) which provides minimal spatial resolution to achieve adequate SNR and 2) the influence of cardiac pulse aliasing on frequency analysis. Long‐Evans rat kidneys ( n =12) were stabilized in vivo, and imaged with the moorFLPI temporal (576×768 pixels, 1Hz) and spatial (113×152 pixels, 25Hz) methods. NxN pixels (N=1…100) in each frame were averaged to generate time‐series, and the power spectrum of each was computed to determine the SNR. We found an adequate SNR was reached at a lower N for the moorFLPI temporal method. Use of the temporal method resulted in cardiac pulse aliasing, but may be used if renal blood flow records are available to identify aliasing. Otherwise, it may be necessary to use the spatial method to prevent aliasing at the expense of spatial resolution. This research was supported by CIHR MOP‐102694.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".